predicting genetic diversity of spontaneous drug-resistance in bacteria - alejandro couce
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LAKE COMO SCHOOL OF ADVANCED STUDIES "QUANTITATIVE LAWS II"
Predicting genetic diversity of spontaneous drug-resistance in bacteria
Alejandro Couce 1,2
1Unité Mixte de Recherche 1137 (IAME-INSERM), Paris, France.
2Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain.
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PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-RESISTANCE IN BACTERIA
Diversity after a drug-induced bottleneck
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PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-RESISTANCE IN BACTERIA
- Short-term diversity
- Long-term diversity
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PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-RESISTANCE IN BACTERIA
- Short-term diversity
- Long-term diversity
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Background
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Mutation, spontaneous or induced?
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Induced mutation
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Spontaneous mutation
Adapted from: Ycart B (2013) PloS One
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Fluctuation test
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The Luria-Delbrück distribution
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What about diversity?
Adapted from: Ycart B (2013) PloS One
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The richness vs evenness paradox
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The richness vs evenness paradox
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Simple, deterministic model
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Simple, deterministic model
r = 5
r = 3
r = 2
r = 1.6
r = 1.2
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High sensitivity to mutant's growth rate
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Impact of 'jackpot' cultures
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Impact of 'jackpot' cultures
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Impact of phenotypic lag
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Impact of phenotypic lag
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Variability on mutant's growth rate
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Variability on mutant's growth rate
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Experimental setting
Small vs Large population size
Presence vs absence of antibiotic
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Experimental system
Resistance to fosfomycin in P. aeruginosa arises from loss-of-function of transporter
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Experimental setting
no AB
with AB
Small vs Large population size
Presence vs absence of antibiotic
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Experimental setting
x
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Experimental setting
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Experimental setting
Couce (2016) Genetics
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PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-RESISTANCE IN BACTERIA
- Short-term diversity
- Long-term diversity
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Adaptive dynamics in bacteria can be complex
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Experimental evolution data
2,000-generations evolution of >100 E. coli populations to low-resource, high-temperature conditions
Olivier et al (2012) Science
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Experimental evolution data
FosR appeared early, and typically stayed at low frequency
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Experimental evolution data
Dynamics suggesting multiple, unsuccesful sweeps
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● Focusing on mutations ≥2% reveals huge divergence
● It highlights the role of historical contingency
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O. Tenaillon lab (Paris, France)
J. Blazquez lab (Madrid, Spain)
Acknowledgements