Understanding the mechanisms governing innovation is a central element of evolutionary theory. Novel traits usually arise through mutations in existing genes, but trade-offs between new and ancestral protein functions are pervasive and constrain the evolution of innovation. Classical models posit that evolutionary innovation circumvents the constraints imposed by trade-offs through genetic amplifications, which provide functional redundancy. Bacterial multicopy plasmids provide a paradigmatic example of genetic amplification, yet their role in evolutionary innovation remains largely unexplored. Here, we reconstructed the evolution of a new trait encoded in a multicopy plasmid using TEM-1 β-lactamase as a model system. Through a combination of theory and experimentation, we show that multicopy plasmids promote the coexistence of ancestral and novel traits for dozens of generations, allowing bacteria to escape the evolutionary constraints imposed by trade-offs. Our results suggest that multicopy plasmids are excellent platforms for evolutionary innovation, contributing to explain their extreme abundance in bacteria.
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Conant, G. C. & Wolfe, K. H. Turning a hobby into a job: how duplicated genes find new functions. Nat. Rev. Genet. 9, 938–950 (2008).
Khersonsky, O. & Tawfik, D. S. Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu. Rev. Biochem. 79, 471–505 (2010).
Toll-Riera, M., San Millan, A., Wagner, A. & MacLean, R. C. The genomic basis of evolutionary innovation in Pseudomonas aeruginosa. PLoS Genet. 12, 1–21 (2016).
Childers, W. S. et al. Cell fate regulation governed by a repurposed bacterial histidine kinase. PLoS Biol. 12, e1001979 (2014).
Anderson, D. P. et al. Evolution of an ancient protein function involved in organized multicellularity in animals. eLife 5, e10147 (2016).
Bershtein, S. & Tawfik, D. S. Ohno’s model revisited: measuring the frequency of potentially adaptive mutations under various mutational drifts. Mol. Biol. Evol. 25, 2311–2318 (2008).
Stiffler, M. A., Hekstra, D. R. & Ranganathan, R. Evolvability as a function of purifying selection in TEM-1 β-lactamase. Cell 160, 882–892 (2015).
Soskine, M. & Tawfik, D. S. Mutational effects and the evolution of new protein functions. Nat. Rev. Genet. 11, 572–582 (2010).
Innan, H. & Kondrashov, F. The evolution of gene duplications: classifying and distinguishing between models. Nat. Rev. Genet. 11, 97–108 (2010).
Andersson, D. I. & Hughes, D. Gene amplification and adaptive evolution in bacteria. Annu. Rev. Genet. 43, 167–195 (2009).
Bergthorsson, U., Andersson, D. I. & Roth, J. R. Ohno’s dilemma: evolution of new genes under continuous selection. Proc. Natl Acad. Sci. USA 104, 17004–17009 (2007).
Pettersson, M. E., Sun, S., Andersson, D. I. & Berg, O. G. Evolution of new gene functions: simulation and analysis of the amplification model. Genetica 135, 309–324 (2009).
Adler, M., Anjum, M., Berg, O. G., Andersson, D. I. & Sandegren, L. High fitness costs and instability of gene duplications reduce rates of evolution of new genes by duplication-divergence mechanisms. Mol. Biol. Evol. 31, 1526–1535 (2014).
Sandegren, L. & Andersson, D. I. Bacterial gene amplification: implications for the evolution of antibiotic resistance. Nat. Rev. Microbiol. 7, 578–588 (2009).
Toussaint, J.-P. et al. Gene duplication in Pseudomonas aeruginosa improves growth on adenosine. J. Bacteriol. 199, e00261-17 (2017).
Näsvall, J., Sun, L., Roth, J. R. & Andersson, D. I. Real-time evolution of new genes by innovation, amplification, and divergence. Science 338, 384–387 (2012).
Stoesser, N. et al. Evolutionary history of the global emergence of the Escherichia coli epidemic clone ST131. mBio 7, e02162 (2016).
Summers, D. K. The Biology of Plasmids (Blackwell, 2009).
Mroczkowska, J. E. & Barlow, M. Fitness trade-offs in blaTEM evolution. Antimicrob. Agents Chemother. 52, 2340–2345 (2008).
Schenk, M. F. et al. Role of pleiotropy during adaptation of TEM-1 β-lactamase to two novel antibiotics. Evol. Appl. 8, 248–260 (2015).
San Millan, A., Escudero, J. A., Gifford, D. R., Mazel, D. & MacLean, R. C. Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria. Nat. Ecol. Evol. 1, 0010 (2016).
Gullberg, E. et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog. 7, e1002158 (2011).
Fuentes-Hernandez, A. et al. Using a sequential regimen to eliminate bacteria at sublethal antibiotic dosages. PLoS Biol. 13, e1002104 (2015).
Kim, S., Lieberman, T. D. & Kishony, R. Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Proc. Natl Acad. Sci. USA 111, 14494–14499 (2014).
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948).
Charlesworth, D. Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet. 2, 379–384 (2006).
Sellis, D., Kvitek, D. J., Dunn, B., Sherlock, G. & Petrov, D. A. Heterozygote advantage is a common outcome of adaptation in Saccharomyces cerevisiae. Genetics 203, 1401–1413 (2016).
Niskanen, A. K. et al. Balancing selection and heterozygote advantage in major histocompatibility complex loci of the bottlenecked Finnish wolf population. Mol. Ecol. 23, 875–889 (2014).
Holloway, A. K., Palzkill, T. & Bull, J. J. Experimental evolution of gene duplicates in a bacterial plasmid model. J. Mol. Evol. 64, 215–222 (2007).
Dhar, R., Bergmiller, T. & Wagner, A. Increased gene dosage plays a predominant role in the initial stages of evolution of duplicate tem-1 beta lactamase genes. Evolution 68, 1775–1791 (2014).
Bedhomme, S., Perez Pantoja, D. & Bravo, I. G. Plasmid and clonal interference during post horizontal gene transfer evolution. Mol. Ecol. 26, 1832–1847 (2017).
Santos-Lopez, A. et al. A naturally occurring SNP in plasmid pB1000 produces a reversible increase in antibiotic resistance. Antimicrob. Agents Chemother. 2, AAC.01735-16 (2016).
Wu, P. J., Shannon, K. & Phillips, I. Mechanisms of hyperproduction of TEM-1 beta-lactamase by clinical isolates of Escherichia coli. J. Antimicrob. Chemother. 36, 927–939 (1995).
Cascales, E. et al. Colicin biology. Microbiol. Mol. Biol. Rev. 71, 158–229 (2007).
Latorre, A., Gil, R., Silva, F. J. & Moya, A. Chromosomal stasis versus plasmid plasticity in aphid endosymbiont Buchnera aphidicola. Heredity 95, 339–347 (2005).
Gomez, A. et al. Creating new genes by plasmid recombination in Escherichia coli and Bacillus subtilis. Appl. Environ. Microbiol. 71, 7607–7609 (2005).
Rodríguez-Beltrán, J. et al. High recombinant frequency in extraintestinal pathogenic Escherichia coli strains. Mol. Biol. Evol. 32, 1708–1716 (2015).
Guttman, D. S. & Dykhuizen, D. E. Clonal divergence in Escherichia coli as a result of recombination, not mutation. Science 266, 1380–1383 (1994).
Gibbons, R. J. & Kapsimalis, B. Estimates of the overall rate of growth of the intestinal microflora of hamsters, guinea pigs, and mice. J. Bacteriol. 93, 510–512 (1967).
Vogwill, T. & Maclean, R. C. The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol. Appl. 8, 284–295 (2015).
San Millan, A. & MacLean, R. C. Fitness costs of plasmids: a limit to plasmid transmission. Microbiol. Spectr. 5, 5 (2017).
Smillie, C., Garcillán-Barcia, M. P., Francia, M. V., Rocha, E. P. C. & de la Cruz, F. Mobility of plasmids. Microbiol. Mol. Biol. Rev. 74, 434–452 (2010).
San Millan, A., Heilbron, K. & MacLean, R. C. Positive epistasis between co-infecting plasmids promotes plasmid survival in bacterial populations. ISME J. 8, 601–612 (2014).
Silva, R. F. et al. Pervasive sign epistasis between conjugative plasmids and drug-resistance chromosomal mutations. PLoS Genet. 7, e1002181 (2011).
Harrison, E. et al. Parallel compensatory evolution stabilizes plasmids across the parasitism–mutualism continuum. Curr. Biol. 25, 2034–2039 (2015).
Porse, A., Schønning, K., Munck, C. & Sommer, M. O. A. Survival and evolution of a large multidrug resistance plasmid in new clinical bacterial hosts. Mol. Biol. Evol. 33, 2860–2873 (2016).
San Millan, A. et al. Positive selection and compensatory adaptation interact to stabilize non-transmissible plasmids. Nat. Commun. 5, 5208 (2014).
Loftie-Eaton, W. et al. Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nat. Ecol. Evol. 1, 1354–1363 (2017).
Tenaillon, O., Taddei, F., Radman, M. & Matic, I. Second-order selection in bacterial evolution: selection acting on mutation and recombination rates in the course of adaptation. Res. Microbiol. 152, 11–16 (2001).
Imamovic, L. & Sommer, M. O. A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci. Transl. Med. 5, 204ra132 (2013).
Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
Leslie, A. G., Moody, P. C. & Shaw, W. V. Structure of chloramphenicol acetyltransferase at 1.75-A resolution. Proc. Natl Acad. Sci. USA 85, 4133–4137 (1988).
Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).
Bonapace, C. R., Bosso, J. A., Friedrich, L. V. & White, R. L. Comparison of methods of interpretation of checkerboard synergy testing. Diagn. Microbiol. Infect. Dis. 44, 363–366 (2002).
Gross, L. A., Baird, G. S., Hoffman, R. C., Baldridge, K. K. & Tsien, R. Y. The structure of the chromophore within DsRed, a red fluorescent protein from coral. Proc. Natl Acad. Sci. USA 97, 11990–11995 (2000).
Performance Standards for Antimicrobial Susceptibility Testing; Twenty-Fourth Informational Supplement (Clinincal and Laboratory Standards Institute, 2014).
Skulj, M. et al. Improved determination of plasmid copy number using quantitative real-time PCR for monitoring fermentation processes. Microb. Cell Fact. 7, 6 (2008).
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).
Summers, D. K. The kinetics of plasmid loss. Trends Biotechnol. 9, 273–278 (1991).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Levin, B. R. & Stewart, F. M. The population biology of bacterial plasmids: a priori conditions for the existence of mobilizable nonconjugative factors. Genetics 94, 425–443 (1980).
We thank R. León-Sampedro for valuable technical assistance with bioinformatic analyses. This work was supported by the Instituto de Salud Carlos III (Plan Estatal de I + D + i 2013–2016): grants CP15-00012, PI16-00860 and CIBER (CB06/02/0053), co-financed by the European Development Regional Fund (ERDF) ‘A way to achieve Europe’. R.P.M. and R.C.M. are supported by a Newton Advanced Fellowship awarded by the Royal Society (NA140196). R.P.M. and A.F.H. are funded by UNAM-PAPIIT (IA201017 and IA201016). R.C.M. was supported by a Wellcome Trust Senior Research Fellowship (WT106918AIA). J.C.R.H.B. is a doctoral student from Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México (UNAM) and received fellowship 596191 from CONACYT. J.A.E. is supported by the Atracción de Talento programme of the Comunidad de Madrid (2016-T1/BIO-1105). A.S.M. is supported by a Miguel Servet Fellowship from the Instituto de Salud Carlos III (MS15/00012) cofinanced by The European Social Fund (ESF) ‘Investing in your future’ and ERDF.
The authors declare no competing interests.
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Supplementary Figs. 1–10, Supplementary Tables 1–3.
Plots representing raw data of the bacterial growth and allelic content in the antibiotic array related to Figs. 2 and 4. The title on each plot denotes the population colonizing the antibiotic array, as well as the antibiotic selection route applied. Optical density (left side) and GFP/RFP ratio (right side) are colour-coded as indicated in the respective legends. The red square denotes the populations that were used to inoculate a fresh antibiotic array on the following day.
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Rodriguez-Beltran, J., Hernandez-Beltran, J.C.R., DelaFuente, J. et al. Multicopy plasmids allow bacteria to escape from fitness trade-offs during evolutionary innovation. Nat Ecol Evol 2, 873–881 (2018). https://doi.org/10.1038/s41559-018-0529-z
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