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Metabolic Erosion Primarily Through Mutation Accumulation, and Not Tradeoffs, Drives Limited Evolution of Substrate Specificity in Escherichia coli

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  • Nicholas Leiby
  • Christopher J Marx

Abstract

: During long-term evolution of Escherichia coli, nutrient specialization is primarily driven by the accumulation of neutral mutations, rather than by tradeoffs, and can also be accompanied by general catabolic improvements. Evolutionary adaptation to a constant environment is often accompanied by specialization and a reduction of fitness in other environments. We assayed the ability of the Lenski Escherichia coli populations to grow on a range of carbon sources after 50,000 generations of adaptation on glucose. Using direct measurements of growth rates, we demonstrated that declines in performance were much less widespread than suggested by previous results from Biolog assays of cellular respiration. Surprisingly, there were many performance increases on a variety of substrates. In addition to the now famous example of citrate, we observed several other novel gains of function for organic acids that the ancestral strain only marginally utilized. Quantitative growth data also showed that strains with a higher mutation rate exhibited significantly more declines, suggesting that most metabolic erosion was driven by mutation accumulation and not by physiological tradeoffs. These reductions in growth by mutator strains were ameliorated by growth at lower temperature, consistent with the hypothesis that this metabolic erosion is largely caused by destabilizing mutations to the associated enzymes. We further hypothesized that reductions in growth rate would be greatest for substrates used most differently from glucose, and we used flux balance analysis to formulate this question quantitatively. To our surprise, we found no significant relationship between decreases in growth and dissimilarity to glucose metabolism. Taken as a whole, these data suggest that in a single resource environment, specialization does not mainly result as an inevitable consequence of adaptive tradeoffs, but rather due to the gradual accumulation of disabling mutations in unused portions of the genome.Author Summary: Adaptation to a single constant environment is commonly expected to result in decreased performance in alternative conditions, or specialization. It has been proposed that, rather than occurring through the neutral accumulation of mutations in unused alternative pathways, this happens because loss of these pathways enhances fitness in the constant environment via “tradeoffs.” We examined growth rates across a variety of nutrients for 12 independent lineages of Escherichia coli that had evolved in the laboratory for decades in a glucose-containing medium. Surprisingly, after 20,000 generations there were actually widespread improvements in the use of alternative nutrients, rather than the expected declines. After 50,000 generations, however, we find that this trend reversed for those populations that evolved a much higher mutation rate. This indicates that high mutation rate, and not adaptive tradeoffs per se (as had been previously proposed), is the primary driver of specialization. These results caution against general assumptions about the importance of adaptive tradeoffs during evolution, and emphasize the key role that newly evolved changes in mutation rate can play in promoting niche specialization.

Suggested Citation

  • Nicholas Leiby & Christopher J Marx, 2014. "Metabolic Erosion Primarily Through Mutation Accumulation, and Not Tradeoffs, Drives Limited Evolution of Substrate Specificity in Escherichia coli," PLOS Biology, Public Library of Science, vol. 12(2), pages 1-10, February.
  • Handle: RePEc:plo:pbio00:1001789
    DOI: 10.1371/journal.pbio.1001789
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    References listed on IDEAS

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    1. Aditya Barve & Andreas Wagner, 2013. "A latent capacity for evolutionary innovation through exaptation in metabolic systems," Nature, Nature, vol. 500(7461), pages 203-206, August.
    2. Zachary D. Blount & Jeffrey E. Barrick & Carla J. Davidson & Richard E. Lenski, 2012. "Genomic analysis of a key innovation in an experimental Escherichia coli population," Nature, Nature, vol. 489(7417), pages 513-518, September.
    3. William R Harcombe & Nigel F Delaney & Nicholas Leiby & Niels Klitgord & Christopher J Marx, 2013. "The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-11, June.
    4. Paul B. Rainey & Michael Travisano, 1998. "Adaptive radiation in a heterogeneous environment," Nature, Nature, vol. 394(6688), pages 69-72, July.
    5. Paul D. Sniegowski & Philip J. Gerrish & Richard E. Lenski, 1997. "Evolution of high mutation rates in experimental populations of E. coli," Nature, Nature, vol. 387(6634), pages 703-705, June.
    6. Vaughn S. Cooper & Richard E. Lenski, 2000. "The population genetics of ecological specialization in evolving Escherichia coli populations," Nature, Nature, vol. 407(6805), pages 736-739, October.
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    Cited by:

    1. Richard E. Lenski & Terence C. Burnham, 2018. "Experimental evolution of bacteria across 60,000 generations, and what it might mean for economics and human decision-making," Journal of Bioeconomics, Springer, vol. 20(1), pages 107-124, April.
    2. Shraddha Karve & Andreas Wagner, 2022. "Environmental complexity is more important than mutation in driving the evolution of latent novel traits in E. coli," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Matteo Mori & Vadim Patsalo & Christian Euler & James R. Williamson & Matthew Scott, 2024. "Proteome partitioning constraints in long-term laboratory evolution," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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