Environmental drivers of a microbial genomic transition zone in the ocean’s interior

Abstract

The core properties of microbial genomes, including GC content and genome size, are known to vary widely among different bacteria and archaea1,2. Several hypotheses have been proposed to explain this genomic variability, but the fundamental drivers that shape bacterial and archaeal genomic properties remain uncertain3,4,5,6,7. Here, we report the existence of a sharp genomic transition zone below the photic zone, where bacterial and archaeal genomes and proteomes undergo a community-wide punctuated shift. Across a narrow range of increasing depth of just tens of metres, diverse microbial clades trend towards larger genome size, higher genomic GC content, and proteins with higher nitrogen but lower carbon content. These community-wide changes in genome features appear to be driven by gradients in the surrounding environmental energy and nutrient fields. Collectively, our data support hypotheses invoking nutrient limitation as a central driver in the evolution of core bacterial and archaeal genomic and proteomic properties.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Quantitative relationships of microbiome genes and taxa and as a function of depth, time and environmental variables at Station ALOHA.
Fig. 2: Microbiome GC content, N-ARSC and C-ARSC versus depth at Station ALOHA.
Fig. 3: Distribution of taxon modules through the GTZ, and with nitrogen concentrations over time at 125 m.

References

  1. 1.

    Ochman, H. & Davalos, L. M. The nature and dynamics of bacterial genomes. Science 311, 1730–1733 (2006).

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    McCutcheon, J. P. & Moran, N. A. Extreme genome reduction in symbiotic bacteria. Nat. Rev. Microbiol. 10, 13–26 (2011).

    PubMed  Google Scholar 

  3. 3.

    Batut, B., Knibbe, C., Marais, G. & Daubin, V. Reductive genome evolution at both ends of the bacterial population size spectrum. Nat. Rev. Microbiol. 12, 841–850 (2014).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Daubin, V. & Moran, N. A. Comment on ‘The origins of genome complexity’. Science 306, 978 (2004).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Giovannoni, S. J. Genome streamlining in a cosmopolitan oceanic bacterium. Science 309, 1242–1245 (2005).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Viklund, J., Ettema, T. J. G. & Andersson, S. G. E. Independent genome reduction and phylogenetic reclassification of the oceanic SAR11 clade. Mol. Biol. Evol. 29, 599–615 (2012).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    DeLong, E. F. et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503 (2006).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Konstantinidis, K. T., Braff, J., Karl, D. M. & DeLong, E. F. Comparative metagenomic analysis of a microbial community residing at a depth of 4,000 meters at station ALOHA in the North Pacific subtropical gyre. Appl. Environ. Microbiol. 75, 5345–5355 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Mizuno, C. M., Ghai, R., Saghaï, A., López-García, P. & Rodriguez-Valera, F. Genomes of abundant and widespread viruses from the deep ocean. mBio 7, e00805–16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333, 1296–1300 (2011).

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Karl, D. M. & Lukas, R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep Sea Res. Part II 43, 129–156 (1996).

    CAS  Article  Google Scholar 

  13. 13.

    Bryant, J. A. et al. Wind and sunlight shape microbial diversity in surface waters of the North Pacific Subtropical Gyre. ISME J. 10, 1308–1322 (2016).

    Article  PubMed  Google Scholar 

  14. 14.

    Laws, E. A., Letelier, R. M. & Karl, D. M. Estimating the compensation irradiance in the ocean: the importance of accounting for non-photosynthetic uptake of inorganic carbon. Deep Sea Res. Part I 93, 35–40 (2014).

    CAS  Article  Google Scholar 

  15. 15.

    Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–884 (2013).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Letelier, R. M., Karl, D. M., Abbott, M. R. & Bidigare, R. R. Light driven seasonal patterns of chlorophyll and nitrate in the lower euphotic zone of the North Pacific Subtropical Gyre. Limnol. Oceanogr. 49, 508–519 (2004).

    CAS  Article  Google Scholar 

  18. 18.

    Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl Acad. Sci. USA 110, 11463–11468 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Biller, S. J., Berube, P. M., Lindell, D. & Chisholm, S. W. Prochlorococcus: the structure and function of collective diversity. Nat. Rev. Microbiol. 13, 13–27 (2015).

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Dupont, C. L. et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 6, 1186–1199 (2012).

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Bragg, J. G. & Hyder, C. L. Nitrogen versus carbon use in prokaryotic genomes and proteomes. Proc. Biol. Sci. 271(Suppl. 5), S374–S377 (2004).

    Article  Google Scholar 

  23. 23.

    Baudouin-Cornu, P., Schuerer, K., Marlière, P. & Thomas, D. Intimate evolution of proteins. Proteome atomic content correlates with genome base composition. J. Biol. Chem. 279, 5421–5428 (2004).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Grzymski, J. J. & Dussaq, A. M. The significance of nitrogen cost minimization in proteomes of marine microorganisms. ISME J. 6, 71–80 (2012).

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

    Article  PubMed  Google Scholar 

  26. 26.

    Rocap, G. et al. Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 424, 1042–1047 (2003).

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Moran, N. A., McCutcheon, J. P. & Nakabachi, A. Genomics and evolution of heritable bacterial symbionts. Annu. Rev. Genet. 42, 165–190 (2008).

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Baudouin-Cornu, P., Surdin-Kerjan, Y., Marlière, P. & Thomas, D. Molecular evolution of protein atomic composition. Science 293, 297–300 (2001).

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Partensky, F. & Garczarek, L. Prochlorococcus: advantages and limits of minimalism. Ann. Rev. Mar. Sci. 2, 305–331 (2010).

    Article  PubMed  Google Scholar 

  30. 30.

    Morris, J. J., Lenski, R. E. & Zinser, E. R. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036–12 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Chevreux, B., Wetter, T. & Suhai, S. Genome sequence assembly using trace signals and additional sequence information. GCB 99, 45–46 (1999).

  32. 32.

    Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Li, W. & Godzik, A. CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Tatusova, T., Ciufo, S., Fedorov, B., O’Neill, K. & Tolstoy, I. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42, D553–D559 (2014).

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    Article  Google Scholar 

  40. 40.

    Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Galperin, M. Y. & Koonin, E. V. Who’s your neighbor? New computational approaches for functional genomics. Nat. Biotechnol. 18, 609–613 (2000).

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927 (2003).

    Article  Google Scholar 

  46. 46.

    Logares, R. et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ. Microbiol. 16, 2659–2671 (2014).

    CAS  Article  PubMed  Google Scholar 

  47. 47.

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

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    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).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Yilmaz, P. et al. The SILVA and "All-species Living Tree Project (LTP)" taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Ramazzotti, M., Berná, L., Donati, C. & Cavalieri, D. riboFrame: an improved method for microbial taxonomy profiling from non-targeted metagenomics. Front. Genet. 6, 329 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Arumugam, M., Harrington, E. D., Foerstner, K. U., Raes, J. & Bork, P. SmashCommunity: a metagenomic annotation and analysis tool. Bioinformatics 26, 2977–2978 (2010).

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Aylward, F. O. et al. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc. Natl Acad. Sci. USA 112, 5443–5448 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, 17 (2005).

    Article  Google Scholar 

  58. 58.

    Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Wright, F. The ‘effective number of codons’ used in a gene. Gene 87, 23–29 (1990).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the captain and crew of the R/V Kilo Moana, and the Hawaii Ocean Time-series marine operations team, for their expert assistance with sample collection and oceanographic data acquisition and analyses at sea. The authors also thank T. Palden, A. Romano and P. Den Uyl for their able assistance in DNA library preparation and DNA sequencing, and B. Barone and L. Fujieki for expert advice and assistance in accessing and displaying HOT oceanographic data sets. The authors also thank S. Sunagawa and G. Zeller for advice and assistance with mOTU analyses. This research was supported by the Simons Foundation (SCOPE award ID 329108 to E.F.D. and D.M.K.), the Gordon and Betty Moore Foundation (through grants GBMF 3777 to E.F.D. and GBMF3794 to D.M.K.) and the National Science Foundation for support of the HOT programme (including the most recent OCE1260164), as well as support to D.R.M. from EMBO (ALTF 721-2015) and the European Commission (LTFCOFUND2013, GA-2013-609409) and support to J.A.B. through the US EPA Science to Achieve Results Fellowship. This work is a contribution of the Simons Collaboration on Ocean Processes and Ecology, and the Center for Microbial Oceanography: Research and Education.

Author information

Affiliations

Authors

Contributions

E.F.D. and D.M.K. designed the overall study, sample and data collection, and analyses. T.N., J.M.E., D.R.M., J.A.B. and F.O.A. performed bioinformatics analyses with input from E.F.D. The manuscript was written by E.F.D., D.R.M., J.A.B. and F.O.A.

Corresponding author

Correspondence to Edward F. DeLong.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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

Electronic supplementary material

Supplementary Information

Supplementary Figures 1–10.

Supplementary Table 1

Sequencing and assembly statistics.

Supplementary Table 2

Physicochemical and biological environmental data.

Supplementary Table 3

Chlorophyll measurements.

Supplementary Table 4

SRA database accession information.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mende, D.R., Bryant, J.A., Aylward, F.O. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat Microbiol 2, 1367–1373 (2017). https://doi.org/10.1038/s41564-017-0008-3

Download citation

Further reading

Search

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing