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Updated: Aug 18, 2025
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Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach
Published on: December 14, 2019
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Segregational instability of multicopy plasmids: A population genetics approach
J Carlos R Hernandez-Beltran1,2, Verónica Miró Pina3,4,5, Arno Siri-Jégousse5
1Systems Biology Program, Center for Genomic Sciences Universidad Nacional Autónoma de México Cuernavaca Mexico.
Ecology and Evolution
|December 8, 2022
Summary
Plasmids, or small DNA circles, can burden bacteria. This study reveals the optimal plasmid copy number balances stability and metabolic cost, acting as a key evolutionary trait for bacterial adaptation.
Area of Science:
- Bacterial genetics
- Population genetics
- Evolutionary biology
Background:
- Plasmids are extrachromosomal genetic elements crucial for bacterial adaptation.
- High plasmid copy numbers increase gene dosage but impose a metabolic burden, reducing host fitness.
- The optimal plasmid copy number represents a trade-off between stability and metabolic cost.
Purpose of the Study:
- To investigate the optimal plasmid copy number in bacterial populations.
- To analyze the interplay between selective pressure, plasmid copy number, and metabolic burden.
- To understand plasmid evolution as an optimized trait.
Main Methods:
- Developed and analyzed a population genetics model.
- Estimated model parameters using *Escherichia coli* K12 with a multicopy plasmid.
- Experimentally assessed plasmid-bearing *E. coli* with resistance to beta-lactam antibiotics.
Main Results:
- Determined optimal plasmid copy numbers under constant and fluctuating selection regimes.
- Showed that plasmid copy number is an optimized evolutionary trait.
- Demonstrated that optimal copy number depends on environmental fluctuation rates.
Conclusions:
- Plasmid copy number is a highly optimized trait balancing stability and metabolic burden.
- Environmental fluctuation rates significantly influence the optimal plasmid copy number.
- Understanding plasmid copy number is vital for predicting bacterial adaptation and evolution.
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