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Cited by 1,216Genomics and Phylogenetic StudiesMicrobial Community Ecology and PhysiologyBacterial Genetics and Biotechnology

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Unusual biology across a group comprising more than 15% of domain Bacteria

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Brown, Christopher T., Hug, Laura A., Thomas, Brian C., Sharon, Itai, Castelle, Cindy J., Singh, Andrea, Wilkins, Michael J., Wrighton, Kelly C., Williams, Kenneth H., Banfield, Jillian F.·2015·Nature
DOI 10.1038/nature14486
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Abstract

A prominent feature of the bacterial domain is a radiation of major lineages that are defined as candidate phyla because they lack isolated representatives. Bacteria from these phyla occur in diverse environments and are thought to mediate carbon and hydrogen cycles. Genomic analyses of a few representatives suggested that metabolic limitations have prevented their cultivation. Here we reconstructed 8 complete and 789 draft genomes from bacteria representing >35 phyla and documented features that consistently distinguish these organisms from other bacteria. We infer that this group, which may comprise >15% of the bacterial domain, has shared evolutionary history, and describe it as the candidate phyla radiation (CPR). All CPR genomes are small and most lack numerous biosynthetic pathways. Owing to divergent 16S ribosomal RNA (rRNA) gene sequences, 50-100% of organisms sampled from specific phyla would evade detection in typical cultivation-independent surveys. CPR organisms often have self-splicing introns and proteins encoded within their rRNA genes, a feature rarely reported in bacteria. Furthermore, they have unusual ribosome compositions. All are missing a ribosomal protein often absent in symbionts, and specific lineages are missing ribosomal proteins and biogenesis factors considered universal in bacteria. This implies different ribosome structures and biogenesis mechanisms, and underlines unusual biology across a large part of the bacterial domain.

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Research works printed but not linked: 56

  1. Harris, J. K., Kelley, S. T. & Pace, N. R. New perspective on uncultured bacterial phylogenetic division OP11. Appl. Environ. Microbiol. 70, 845–849 (2004). · DOI
  2. Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012). · DOI
  3. Kantor, R. S. et al. Small genomes and sparse metabolisms of sediment-associated bacteria from four candidate phyla. MBio 4, e00708–e00713 (2013). · DOI
  4. Wrighton, K. C. et al. Metabolic interdependencies between phylogenetically novel fermenters and respiratory organisms in an unconfined aquifer. ISME J. 8, 1452–1463 (2014). · DOI
  5. Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013). · DOI · Paper page
  6. Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nature Biotechnol. 31, 533–538 (2013). · DOI
  7. Luef, B. et al. Diverse, uncultivated ultra-small bacterial cells in groundwater. Nature Commun. 6, 6372 (2015). · DOI
  8. Burt, A. & Koufopanou, V. Homing endonuclease genes: the rise and fall and rise again of a selfish element. Curr. Opin. Genet. Dev. 14, 609–615 (2004). · DOI
  9. Salman, V., Amann, R., Shub, D. A. & Schulz-Vogt, H. N. Multiple self-splicing introns in the 16S rRNA genes of giant sulfur bacteria. Proc. Natl Acad. Sci. USA 109, 4203–4208 (2012). · DOI
  10. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013). · DOI · Paper page
  11. Evguenieva-Hackenberg, E. Bacterial ribosomal RNA in pieces. Mol. Microbiol. 57, 318–325 (2005). · DOI · Paper page
  12. Raghavan, R., Hicks, L. D. & Minnick, M. F. Toxic introns and parasitic intein in Coxiella burnetii: legacies of a promiscuous past. J. Bacteriol. 190, 5934–5943 (2008). · DOI
  13. Baker, B. J., Hugenholtz, P., Dawson, S. C. & Banfield, J. F. Extremely acidophilic protists from acid mine drainage host Rickettsiales-lineage endosymbionts that have intervening sequences in their 16S rRNA genes. Appl. Environ. Microbiol. 69, 5512–5518 (2003). · DOI
  14. Gong, J., Qing, Y., Guo, X. & Warren, A. ‘Candidatus Sonnebornia yantaiensis’, a member of candidate division OD1, as intracellular bacteria of the ciliated protist Paramecium bursaria (Ciliophora, Oligohymenophorea). Syst. Appl. Microbiol. 37, 35–41 (2014). · DOI
  15. Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012). · DOI · Paper page
  16. Nawrocki, E. P. in Structural RNA Homology Search and Alignment using Covariance Models (ed. Eddy, S. R. et al.) (Washington Univ. in Saint Louis, 2009).
  17. Baker, B. J. & Dick, G. J. Omic approaches in microbial ecology: charting the unknown. Microbe 8, 353–360 (2013).
  18. Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nature Rev. Microbiol. 12, 635–645 (2014). · DOI
  19. Akanuma, G. et al. Inactivation of ribosomal protein genes in Bacillus subtilis reveals importance of each ribosomal protein for cell proliferation and cell differentiation. J. Bacteriol. 194, 6282–6291 (2012). · DOI
  20. Lecompte, O. Comparative analysis of ribosomal proteins in complete genomes: an example of reductive evolution at the domain scale. Nucleic Acids Res. 30, 5382–5390 (2002). · DOI
  21. Lagkouvardos, I., Jehl, M.-A., Rattei, T. & Horn, M. Signature protein of the PVC superphylum. Appl. Environ. Microbiol. 80, 440–445 (2014). · DOI
  22. Yutin, N., Puigbò, P., Koonin, E. V. & Wolf, Y. I. Phylogenomics of prokaryotic ribosomal proteins. PLoS ONE 7, e36972 (2012). · DOI
  23. Nowotny, V. & Nierhaus, K. H. Initiator proteins for the assembly of the 50S subunit from Escherichia coli ribosomes. Proc. Natl Acad. Sci. USA 79, 7238–7242 (1982). · DOI
  24. Atkins, J. F. & Björk, G. R. A gripping tale of ribosomal frameshifting: extragenic suppressors of frameshift mutations spotlight P-site realignment. Microbiol. Mol. Biol. Rev. 73, 178–210 (2009). · DOI
  25. Schuwirth, B. S. Structures of the bacterial ribosome at 3.5 Å resolution. Science 310, 827–834 (2005). · DOI
  26. Nevskaya, N. Ribosomal protein L1 recognizes the same specific structural motif in its target sites on the autoregulatory mRNA and 23S rRNA. Nucleic Acids Res. 33, 478–485 (2005). · DOI
  27. Shajani, Z., Sykes, M. T. & Williamson, J. R. Assembly of bacterial ribosomes. Annu. Rev. Biochem. 80, 501–526 (2011). · DOI
  28. Luef, B. et al. Iron-reducing bacteria accumulate ferric oxyhydroxide nanoparticle aggregates that may support planktonic growth. ISME J. 7, 338–350 (2013). · DOI
  29. Williams, K. H. et al. Acetate availability and its influence on sustainable bioremediation of uranium-contaminated groundwater. Geomicrobiol. J. 28, 519–539 (2011). · DOI
  30. Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012). · DOI
  31. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357–359 (2012). · DOI · Paper page
  32. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010). · DOI
  33. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010). · DOI
  34. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012). · DOI · Paper page
  35. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27 (2000). · DOI · Paper page
  36. Hug, L. A. et al. Community genomic analyses constrain the distribution of metabolic traits across the Chloroflexi phylum and indicate roles in sediment carbon cycling. Microbiome 1, 22 (2013). · DOI
  37. Castelle, C. J. et al. Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment. Nature Commun. 4, 2120 (2013). · DOI
  38. Dick, G. J. et al. Community-wide analysis of microbial genome sequence signatures. Genome Biol. 10, R85 (2009). · DOI
  39. Raes, J., Korbel, J. O., Lercher, M. J., von Mering, C. & Bork, P. Prediction of effective genome size in metagenomic samples. Genome Biol. 8, R10 (2007). · DOI
  40. McLean, J. S. et al. Candidate phylum TM6 genome recovered from a hospital sink biofilm provides genomic insights into this uncultivated phylum. Proc. Natl Acad. Sci. USA 110, E2390–E2399 (2013). · DOI
  41. Podar, M. et al. Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl. Environ. Microbiol. 73, 3205–3214 (2007). · DOI
  42. Marcy, Y. et al. Dissecting biological ‘dark matter’ with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl Acad. Sci. USA 104, 11889–11894 (2007). · DOI
  43. Nawrocki, E. P., Kolbe, D. L. & Eddy, S. R. Infernal 1.0: inference of RNA alignments. Bioinformatics 25, 1335–1337 (2009). · DOI
  44. Cannone, J. J. et al. The Comparative RNA Web (CRW) Site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinformatics 3, 2 (2002). · DOI
  45. Burge, S. W. et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41, D226–D232 (2013). · DOI
  46. Andronescu, M., Condon, A., Hoos, H. H., Mathews, D. H. & Murphy, K. P. Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics 23, i19–i28 (2007). · DOI
  47. Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012). · DOI
  48. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014). · DOI · Paper page
  49. Kelley, L. A. & Sternberg, M. J. E. Protein structure prediction on the Web: a case study using the Phyre server. Nature Protocols 4, 363–371 (2009). · DOI
  50. Gilbert, J. A. et al. Meeting report: the terabase metagenomics workshop and the vision of an Earth microbiome project. Stand. Genomic Sci. 3, 243–248 (2010). · DOI
  51. Walters, W. A. et al. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics 27, 1159–1161 (2011). · DOI
  52. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014). · DOI · Paper page
  53. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004). · DOI · Paper page
  54. Abascal, F., Zardoya, R. & Posada, D. ProtTest: selection of best-fit models of protein evolution. Bioinformatics 21, 2104–2105 (2005). · DOI · Paper page
  55. Huson, D. H. & Scornavacca, C. Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Syst. Biol. 61, 1061–1067 (2012). · DOI
  56. Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008). · DOI

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Metadata

Authors
Brown, C. T., Hug, L. A., Thomas, B. C., Sharon, I., Castelle, C. J., Singh, A., Wilkins, M. J., Wrighton, K. C., Williams, K. H., & Banfield, J. F.
Affiliations
  • University of California, Berkeley (US)
  • The Ohio State University (US)
  • Lawrence Berkeley National Laboratory (US)
Corresponding author
Banfield, Jillian F.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2015-06-15
Volume
523
Issue
7559
Pages
208-211
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/nature14486
OpenAlex
W1528357204
Cited by
1,216
References
63
Citations by year
2026: 56 · 2025: 100 · 2024: 95 · 2023: 125 · 2022: 108 · 2021: 120 · 2020: 144 · 2019: 121 · 2018: 116 · 2017: 104 · 2016: 101 · 2015: 26
FWCI
50.17
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • U.S. Department of Energy (DESC0004918, DE AC02 05CH11231)
  • Joint Genome Institute
  • Office of Science
  • Biological and Environmental Research
  • Pacific Northwest National Laboratory
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
Keywords
Candidate Phyla Radiation, uncultured bacteria, bacterial evolution, ribosome composition, ribosome biogenesis, self-splicing introns, small genomes, groundwater bacteria, metagenome-assembled genomes, symbionts
MeSH terms
Bacteria, Environmental Microbiology, Introns, Phylogeny, Ribosomal Proteins, RNA, Ribosomal, 16S, Genome, Bacterial

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