Abstract
Codon usage is the outcome of different evolutionary processes and can inform us about the conditions in which organisms live and evolve. Here, we present R_ENC’, which is an improvement to the original S index developed by dos Reis et al. (2004). Our index is less sensitive to G+C content, which greatly affects synonymous codon usage in prokaryotes, making it better suited to detect selection acting on codon usage. We used R_ENC’ to estimate the extent of selected codon usage bias in 1800 genomes representing 26 prokaryotic phyla. We found that Gammaproteobacteria, Betaproteobacteria, Actinobacteria, and Firmicutes are the phyla/subphyla showing more genomes with selected codon usage bias. In particular, we found that several lineages within Gammaproteobacteria and Firmicutes show a similar set of functional terms enriched in genes under selected codon usage bias, indicating convergent evolution. We also show that selected codon usage bias tends to evolve in genes coding for the translation machinery before other functional GO terms. Finally, we discuss the possibility to use R_ENC’ to predict whether lineages evolved in copiotrophic or oligotrophic environments.
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References
Alexa A, Rahnenfuhrer J (2021) topGO: enrichment analysis for gene ontology. R Package Version. https://doi.org/10.18129/B9.bioc.topGO
Arella D, Dilucca M, Giansanti A (2021) Codon usage bias and environmental adaptation in microbial organisms. Mol Genet Genomics 296(3):751–762. https://doi.org/10.1007/s00438-021-01771-4
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556
Botzman M, Margalit H (2011) Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles. Genome Biol 12(10):R109. https://doi.org/10.1186/gb-2011-12-10-r109
Bulmer M (1991) The selection-mutation-drift theory of synonymous codon usage. Genetics 129(3):897–907. https://doi.org/10.1093/genetics/129.3.897
Carbone A, Madden R (2005) Insights on the evolution of metabolic networks of unicellular translationally biased organisms from transcriptomic data and sequence analysis. J Mol Evol 61(4):456–469. https://doi.org/10.1007/s00239-004-0317-z
dos Reis MD, Savva R, Wernisch L (2004) Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 32(17):5036–5044. https://doi.org/10.1093/nar/gkh834
Freilich S, Kreimer A, Borenstein E, Yosef N, Sharan R, Gophna U, Ruppin E (2009) Metabolic-network-driven analysis of bacterial ecological strategies. Genome Biol 10(6):1–8. https://doi.org/10.1186/gb-2009-10-6-r61
Frumkin I, Lajoie MJ, Gregg CJ, Hornung G, Church GM, Pilpel Y (2018) Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proc Natl Acad Sci USA 115(21):E4940–E4949. https://doi.org/10.1073/pnas.1719375115
Fuglsang A (2006) Estimating the “effective number of codons”: the wright way of determining codon homozygosity leads to superior estimates. Genetics 172(2):1301–1307. https://doi.org/10.1534/genetics.105.049643
Grantham R, Gautier C, Gouy M, Jacobzone M, Mercier R (1981) Codon catalog usage is a genome strategy modulated for gene expressivity. Nucleic Acids Res 9(1):213–213. https://doi.org/10.1093/nar/9.1.213-b
Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59(3):307–321. https://doi.org/10.1093/sysbio/syq010
Gustafsson C, Govindarajan S, Minshull J (2004) Codon bias and heterologous protein expression. Trends Biotechnol 22(7):346–353. https://doi.org/10.1016/j.tibtech.2004.04.006
Ikemura T (1981a) Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes. J Mol Biol 146(1):1–21. https://doi.org/10.1016/0022-2836(81)90363-6
Ikemura T (1981b) Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J Mol Biol 151(3):389–409. https://doi.org/10.1016/0022-2836(81)90003-6
Ikemura T (1985) Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol 2(1):13–34. https://doi.org/10.1093/oxfordjournals.molbev.a040335
Iriarte A, Lamolle G, Musto H (2021) Codon usage bias: an endless tale. J Mol Evol 89:589–593. https://doi.org/10.1007/s00239-021-10027-z
Karlin S, Mrázek J, Campbell A, Kaiser D (2001) Characterizations of highly expressed genes of four fast-growing bacteria. J Bacteriol 183(17):5025–5040
Klumpp S, Scott M, Pedersen S, Hwa T (2013) Molecular crowding limits translation and cell growth. Proc Natl Acad Sci USA 110(42):16754–16759. https://doi.org/10.1073/pnas.1310377110
Koch AL (2001) Oligotrophs versus copiotrophs. BioEssays 23(7):657–661. https://doi.org/10.1002/bies.1091
Kudla G, Murray AW, Tollervey D, Plotkin JB (2009) Coding-sequence determinants of gene expression in Escherichia coli. Science 324:255–258. https://doi.org/10.1126/science.1170160
Lefort V, Longueville JE, Gascuel O (2017) SMS: smart model selection in PhyML. Mol Biol Evol 34(9):2422–2424. https://doi.org/10.1093/molbev/msx149
Liu SS, Hockenberry AJ, Jewett MC, Amaral LA (2018) A novel framework for evaluating the performance of codon usage bias metrics. J R Soc Interface 15(138):20170667. https://doi.org/10.1098/rsif.2017.0667
Louca S, Doebeli M (2018) Efficient comparative phylogenetics on large trees. Bioinformatics 34(6):1053–1055. https://doi.org/10.1093/bioinformatics/btx701
Martínez-Cano DJ, Bor G, Moya A, Delaye L (2018) Testing the domino theory of gene loss in Buchnera aphidicola: the relevance of epistatic interactions. Life (basel) 8(2):17. https://doi.org/10.3390/life8020017
Molenaar D, van Berlo R, de Ridder D, Teusink B (2009) Shifts in growth strategies reflect tradeoffs in cellular economics. Mol Syst Biol 5:323. https://doi.org/10.1038/msb.2009.82
Mori M, Schink S, Erickson DW, Gerland U, Hwa T (2017) Quantifying the benefit of a proteome reserve in fluctuating environments. Nat Commun 8(1):1225. https://doi.org/10.1038/s41467-017-01242-8
Novembre JA (2002) Accounting for background nucleotide composition when measuring codon usage bias. Mol Biol Evol 19(8):1390–1394. https://doi.org/10.1093/oxfordjournals.molbev.a004201
Okie JG, Poret-Peterson AT, Lee ZM, Richter A, Alcaraz LD, Eguiarte LE et al (2020) Genomic adaptations in information processing underpin trophic strategy in a whole-ecosystem nutrient enrichment experiment. Elife 9:e49816. https://doi.org/10.7554/eLife.49816
Orme D (2018) The Caper Package: comparative analysis of phylogenetics and evolution in R (version 1.0. 1). https://cran.r-project.org/web/packages/caper.
Parvathy ST et al (2022) Codon usage bias. Mol Biol Rep 49(1):539–565. https://doi.org/10.1007/s11033-021-06749-4
Peebo K, Valgepea K, Maser A, Nahku R, Adamberg K, Vilu R (2015) Proteome reallocation in Escherichia coli with increasing specific growth rate. Mol BioSyst 11(4):1184–1193. https://doi.org/10.1039/C4MB00721B
Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12(1):32. https://doi.org/10.1038/nrg2899
Quax TE, Claassens NJ, Söll D, van der Oost J (2015) Codon bias as a means to fine-tune gene expression. Mol Cell 59(2):149–161. https://doi.org/10.1016/j.molcel.2015.05.035
Ran W, Higgs PG (2012) Contributions of speed and accuracy to translational selection in bacteria. PLoS ONE 7(12):e51652. https://doi.org/10.1371/journal.pone.0051652
Revell LJ (2012) phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol 3:217–223. https://doi.org/10.1111/j.2041-210X.2011.00169.x
Rocha EP (2004) Codon usage bias from tRNA’s point of view: redundancy, specialization, and efficient decoding for translation optimization. Genome Res 14(11):2279–2286. https://doi.org/10.1101/gr.2896904
Roller BR, Schmidt TM (2015) The physiology and ecological implications of efficient growth. ISME J 9(7):1481–1487. https://doi.org/10.1038/ismej.2014.235
Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T (2010) Interdependence of cell growth and gene expression: origins and consequences. Science 330:1099–1102. https://doi.org/10.1126/science.1192588
Segata N, Börnigen D, Morgan XC, Huttenhower C (2013) PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nat Commun 4:2304. https://doi.org/10.1038/ncomms3304
Sharp PM, Bailes E, Grocock RJ, Peden JF, Sockett RE (2005) Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Res 33(4):1141–1153. https://doi.org/10.1093/nar/gki242
Sharp PM, Emery LR, Zeng K (2010) Forces that influence the evolution of codon bias. Philos Trans R Soc Lond B Biol Sci 365(1544):1203–1212. https://doi.org/10.1098/rstb.2009.0305
Supek F, Bošnjak M, Škunca N, Šmuc T (2011) REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6(7):e21800. https://doi.org/10.1371/journal.pone.0021800
Thiele I, Fleming R, Que R, Bordbar A, Palsson B (2011) A systems biology approach to the evolution of codon use pattern. Nat Prec. https://doi.org/10.1038/npre.2011.6312.1
UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47(D1):D506–D515. https://doi.org/10.1093/nar/gky1049
Vieira-Silva S, Rocha EP (2010) The systemic imprint of growth and its uses in ecological (meta) genomics. PLoS Genet 6(1):e1000808. https://doi.org/10.1371/journal.pgen.1000808
Yang Z, Goldman N, Friday A (1995) Maximum likelihood trees from DNA sequences: a peculiar statistical estimation problem. Syst Biol 44(3):384–399. https://doi.org/10.1093/sysbio/44.3.384
Wei Y, Silke JR, Xia X (2019) An improved estimation of tRNA expression to better elucidate the coevolution between tRNA abundance and codon usage in bacteria. Sci Rep 9(1):3184. https://doi.org/10.1038/s41598-019-39369-x
Wright F (1990) The ‘effective number of codons’ used in a gene. Gene 87(1):23–29. https://doi.org/10.1016/0378-1119(90)90491-9
Zavřel T, Faizi M, Loureiro C, Poschmann G, Stühler K, Sinetova M et al (2019) Quantitative insights into the cyanobacterial cell economy. eLife 8:e42508. https://doi.org/10.7554/eLife.42508.001
Acknowledgements
We are indebted to CONACYT for supporting Francisco González during his master's degree in Integrative Biology in Cinvestav Irapuato (CVU: 856429). We also thank UGA/LANGEBIO for giving access to its HPC (High-Performance Computing Cluster) “MAZORKA” and to LAICBIO for providing computer facilities.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by FG-S. LD directed the analyses and CA-G helped evaluate the results. The first draft of the manuscript was written by FG-S and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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González-Serrano, F., Abreu-Goodger, C. & Delaye, L. Translation Comes First: Ancient and Convergent Selection of Codon Usage Bias Across Prokaryotic Genomes. J Mol Evol 90, 438–451 (2022). https://doi.org/10.1007/s00239-022-10074-0
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DOI: https://doi.org/10.1007/s00239-022-10074-0