Effective polyploidy causes phenotypic delay and influences bacterial evolvability
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Abstract
Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s-50s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite lack of direct evidence. Here we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of 3-4 generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copy or per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation, and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution. Author summary What is the time delay between the occurrence of a genetic mutation in a bacterial cell and manifestation of its phenotypic effect? We show that antibiotic resistance mutations in E.coli show a remarkably long phenotypic delay of 3-4 bacterial generations. The primary underlying mechanism of this delay is effective polyploidy. In a polyploid cell with multiple chromosomes, once a mutation arises on one of the chromosomes, the presence of non-mutated, wild-type gene copies on other chromosomes may mask the phenotype of the mutation. One implication of this finding is that conventional methods to determine the mutation rates of bacteria do not detect polyploidy and thus underestimate their potential for adaptation. More generally, the effect that a new mutation may become useful only in the “grand-children of the grand-children” suggests that pre-existing mutations are more important for surviving sudden environmental catastrophe.
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rSalvador is the most accurate tool for mutation rate estimation.
“Based upon simulations, rSalvador is the most accurate.”
From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [24]
In Materials and methods (Simulated fluctuation tests; page unavailable), the paper says it used its own R code and mentions rSalvador only as a package into which its methods might later be integrated. Its simulations do not compare the accuracy of mutation-rate estimation tools, so they do not establish that rSalvador is the most accurate. (LLM, conf=0.76)
Experimental — accuracy not yet measuredStewart's correction gives acceptable point estimates only for large values of ε.
“As was shown by Zheng, Stewart's correction gives acceptable point estimates only for large values of ε [24].”
From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [24]
The Materials and methods section on simulated fluctuation tests describes maximum-likelihood mutation-rate estimates, and the Results section discusses how those estimates vary with ploidy and mutation dominance (pages unavailable). Neither addresses Stewart’s correction or whether it gives acceptable point estimates only at large ε; searches for Stewart, Zheng, and the ε condition found no such passage in our copy. (LLM, conf=0.76)
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Cites
Hall, B. M., Ma, C.-X., Liang, P., & Singh, K. K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp253 · Cited by 375 · see citing works
Sarkar, S., Ma, W. T., & Sandri, G. V. H. (1992) · Genetica · doi:10.1007/bf00120324 · Cited by 181 · see citing works
rSalvador: An R Package for the Fluctuation Experiment
Zheng, Q. (2017) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.117.300120 · Cited by 98 · cited 1× · see citing works
“We wrote our own code to account for polyploidy, but in the future, our methods could potentially be integrated into recently published R packages for fluctuation analysis [47,48].”
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Carballo-Pacheco, M., Nicholson, M. D., Lilja, E. E., Allen, R. J., & Waclaw, B. (2020) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1007930 · Cited by 18 · see citing works
Łazowski, K. (2023) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2023.111816 · Cited by 10 · see citing works
- Authors
- Sun, L., Alexander, H. K., Bogos, B., Kiviet, D. J., Ackermann, M., & Bonhoeffer, S.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Bonhoeffer, Sebastian
- Venue
- PLOS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-02-22
- Volume
- 16
- Issue
- 2
- Pages
- e2004644
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC5839593
- OpenAlex
- W2952558578
- Cited by
- 53
- References
- 3
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 7 · 2020: 17 · 2019: 5 · 2018: 2
- FWCI
- 4.90
- Type
- journal article
- Language
- en
- Update notices
- new_version (2018-03-06)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DORA Eawag (Swiss Federal Institute of Aquatic Science and Technology (Eawag)) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- National Science Foundation
- European Commission (268540)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (155866, 268540, 149267)
- Eidgenössische Technische Hochschule Zürich
- Uppsala Universitet
- FP7 Ideas: European Research Council (268540)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- antibiotic resistance evolution, recessive mutations, standing genetic variation, mutation rate estimation, fluctuation test, Escherichia coli, recombineering, adaptation to environmental change
- MeSH terms
- Chromosomes, Bacterial, DNA Replication, DNA, Bacterial, Escherichia coli, Genes, Bacterial, Genes, Recessive, Mutation, Polyploidy, Genetic Variation, Mutagenesis, Gene Dosage, Replication Origin, Evolution, Molecular, Drug Resistance, Bacterial
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