QUANTITATIVE LAWS June 13 -June 24

1. 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.

2. PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-
RESISTANCE IN BACTERIA
Diversity after a drug-induced bottleneck

3. PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-
RESISTANCE IN BACTERIA
- Short-term diversity
- Long-term diversity

4. PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-
RESISTANCE IN BACTERIA
- Short-term diversity
- Long-term diversity

5. Background

6. Mutation, spontaneous or induced?

7. Induced mutation

8. Spontaneous mutation
Adapted from: Ycart B (2013) PloS One

9. Fluctuation test

10. The Luria-Delbrück distribution

11. What about diversity?
Adapted from: Ycart B (2013) PloS One

12. The richness vs evenness paradox

13. The richness vs evenness paradox

14. Simple, deterministic model

15. Simple, deterministic model
r = 5
r = 3
r = 2
r = 1.6
r = 1.2

16. High sensitivity to mutant's growth rate

17. Impact of 'jackpot' cultures

18. Impact of 'jackpot' cultures

19. Impact of phenotypic lag

20. Impact of phenotypic lag

21. Variability on mutant's growth rate

22. Variability on mutant's growth rate

23. Experimental setting
Small vs Large
population size
Presence vs absence
of antibiotic

24. Experimental system
Resistance to fosfomycin in P. aeruginosa arises from
loss-of-function of transporter

25. Experimental setting
no AB
with AB
Small vs Large
population size
Presence vs absence
of antibiotic

26. Experimental setting
x

27. Experimental setting

28. Experimental setting
Couce (2016) Genetics

29. PREDICTING GENETIC DIVERSITY OF SPONTANEOUS DRUG-
RESISTANCE IN BACTERIA
- Short-term diversity
- Long-term diversity

30. Adaptive dynamics in bacteria can be
complex

31. Experimental evolution data
2,000-generations evolution of >100 E. coli populations
to low-resource, high-temperature conditions
Olivier et al (2012) Science

32. Experimental evolution data
FosR appeared early, and typically stayed at low frequency

33. Experimental evolution data
Dynamics suggesting multiple, unsuccesful sweeps

34. ● Focusing on
mutations ≥2%
reveals huge
divergence
● It highlights the
role of historical
contingency

35. O. Tenaillon lab
(Paris, France)
J. Blazquez lab
(Madrid, Spain)
Acknowledgements