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