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PreprintCited by 67RNA and protein synthesis mechanismsEvolution and Genetic DynamicsGenomics and Phylogenetic Studies

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Resource conservation manifests in the genetic code

Open access
Shenhav, Liat, Zeevi, David·2019·Science
DOI 10.1101/790345

Preprint — published as 10.1126/science.aaz9642

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Abstract

Nutrient limitation is a strong selective force, driving competition for resources. However, much is unknown about how selective pressures resulting from nutrient limitation shape microbial coding sequences. Here, we study this ‘resource-driven’ selection using metagenomic and single-cell data of marine microbes, alongside environmental measurements. We show that a significant portion of the selection exerted on microbes is explained by the environment and is strongly associated with nitrogen availability. We further demonstrate that this resource conservation optimization is encoded in the structure of the standard genetic code, providing robustness against mutations that increase carbon and nitrogen incorporation into protein sequences. Overall, we demonstrate that nutrient conservation exerts a significant selective pressure on coding sequences and may have even contributed to the evolution of the genetic code.

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Research works printed but not linked: 57

  1. 10.7554/eLife.41043 · DOI
  2. 2017, Environmental drivers of a microbial genomic transition zone in the ocean’s interior, Nature Microbiology, 2, 1367, 10.1038/s41564-017-0008-3 · DOI
  3. 10.1038/ismej.2011.72 · DOI
  4. 10.1073/pnas.062526999 · DOI
  5. 2018, Carbon limitation drives GC content evolution of a marine bacterium in an individual-based genome-scale model, The ISME Journal, 12, 1180, 10.1038/s41396-017-0023-7 · DOI
  6. 2016, Radiocarbon in dissolved organic carbon of the Atlantic Ocean, Geophys. Res. Lett, 43, 5279, 10.1002/2016GL068746 · DOI
  7. 10.1126/science.142.3595.1083 · DOI · Paper page
  8. 10.1038/173318a0 · DOI
  9. 10.1073/pnas.72.5.1909 · DOI · Paper page
  10. 10.1007/PL00006381 · DOI · Paper page
  11. 10.1073/pnas.55.4.966 · DOI · Paper page
  12. 10.1007/s00239-008-9167-4 · DOI · Paper page
  13. A. S. Novozhilov , E. V. Koonin , Exceptional error minimization in putative primordial genetic codes. Biol. Direct. 4, 44 (2009).
  14. 2007, The case for an error minimizing set of coding amino acids, J. Theor. Biol, 244, 737, 10.1016/j.jtbi.2006.09.021 · DOI · Paper page
  15. 10.1007/BF02103132 · DOI · Paper page
  16. S. Sunagawa et al., Structure and function of the global ocean microbiome. Science. 348, 1261359 (2015).
  17. S. J. Biller et al., Marine microbial metagenomes sampled across space and time. Sci Data. 5, 180176 (2018).
  18. 10.1093/nar/28.1.27 · DOI · Paper page
  19. 10.1093/molbev/msx148. · DOI · Paper page
  20. 2019, Charting the Complexity of the Marine Microbiome through Single-Cell Genomics, Cell, 179, 1623, 10.1016/j.cell.2019.11.017 · DOI
  21. A. Dufresne , L. Garczarek , F. Partensky , Accelerated evolution associated with genome reduction in a free-living prokaryote. Genome Biol. 6, R14 (2005).
  22. 10.1038/s41396-019-0472-2 · DOI
  23. 10.1007/PL00006294 · DOI
  24. 10.1186/gb-2008-9-4-r69 · DOI
  25. 10.1038/ismej.2017.88 · DOI
  26. 10.1126/science.170.3960.822 · DOI
  27. 10.1111/j.1601-5223.1998.00173.x · DOI
  28. M. Botzman , H. Margalit , Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles. Genome Biol. 12, R109 (2011).
  29. M. Yarus , J. J. Widmann , R. Knight , RNA–Amino Acid Binding: A Stereochemical Era for the Genetic Code. J. Mol. Evol. 69, 406 (2009).
  30. 10.1016/0022-2836(68)90392-6 · DOI · Paper page
  31. 10.1093/oxfordjournals.molbev.a026331 · DOI
  32. 10.1038/s41586-019-1065-y · DOI · Paper page
  33. 10.1038/nature09534 · DOI · Paper page
  34. 10.1093/nar/gky1085 · DOI
  35. 10.1093/bioinformatics/btu153 · DOI
  36. 10.1093/nar/28.1.45 · DOI
  37. 10.2307/2282967 · DOI
  38. 10.1093/molbev/mst010 · DOI · Paper page
  39. 10.1093/bioinformatics/btu033 · DOI · Paper page
  40. 10.1093/oxfordjournals.molbev.a040153 · DOI · Paper page
  41. 2018, The GEOTRACES Intermediate Data Product 2017, Chem. Geol, 493, 210, 10.1016/j.chemgeo.2018.05.040 · DOI
  42. 10.1038/nrg2813 · DOI
  43. 10.1038/nature11711 · DOI · Paper page
  44. M. R. E. Symonds , S. P. Blomberg , A Primer on Phylogenetic Generalised Least Squares. Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (2014), pp. 105–130.
  45. 10.2307/2261605 · DOI
  46. 10.1093/bioinformatics/btu668 · DOI · Paper page
  47. J. Athey et al., A new and updated resource for codon usage tables. BMC Bioinformatics. 18, 391 (2017).
  48. 2000, Rates of conservative and radical nonsynonymous nucleotide substitutions in mammalian nuclear genes, J. Mol. Evol, 50, 56, 10.1007/s002399910007 · DOI · Paper page
  49. 10.1126/science.1114057 · DOI
  50. 10.1126/science.1254421 · DOI
  51. 10.1073/pnas.1304246110 · DOI
  52. 2011, Quantifying E. coli Proteome and Transcriptome with Single-molecule Sensitivity in Single Cells, Seibutsu Butsuri, 51, 136, 10.2142/biophys.51.136 · DOI
  53. 10.1029/1999GB900013 · DOI
  54. 10.1038/nrg2526 · DOI
  55. 10.1126/science.290.5494.1151 · DOI · Paper page
  56. 10.1093/molbev/msn166 · DOI
  57. 10.1038/351652a0 · DOI

Metadata

Authors
Shenhav, L., & Zeevi, D.
Affiliations
  • University of California, Los Angeles (US)
  • Rockefeller University (US)
Corresponding author
Zeevi, David
ORCID
Venue
Science
Publisher
openRxiv
Published
2019-10-02
Volume
370
Issue
6517
Pages
683-687
ISSN
0036-8075 (ISSN-L)
DOI
10.1101/790345
OpenAlex
W3095462271
Cited by
67
References
1
Citations by year
2026: 7 · 2025: 12 · 2024: 14 · 2023: 12 · 2022: 7 · 2021: 13 · 2020: 2
FWCI
2.76
Type
journal article
Language
en
Open access
Locations
  • Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
Funders
James S. McDonnell Foundation (220020500)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
genetic code, resource conservation, nutrient limitation, nitrogen availability, codon usage, marine microorganisms, metagenomics, single-cell genomics, environmental selection, protein synthesis, mutation robustness, human genome
MeSH terms
Codon Usage, Carbon, Conservation of Natural Resources, Genetic Code, Nitrogen, Seawater, Selection, Genetic, Synechococcus, Prochlorococcus, Metagenome, Metagenomics, Single-Cell Analysis

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