1. The Problem of Drug Resistance
E3: Lecture 11

2. Early Antibiotic Research
Joseph Lister ( )
- In 1871, he noted that samples of urine with mold did not permit bacterial growth.
- He also pioneered the introduction of an antiseptic (phenol) before and during surgery, drastically reducing the rate of infection.
Ernest Duchesne ( )
In 1897, he demonstrated that E. coli was killed when cultured with Penicillium glaucum.
He also showed that injection of this mold into animals infected with typhoid bacilli prevented the advent of the disease.
Alexander Flemming ( )
In 1928, noticed a mold contaminant on a bacterial plate that had been sitting out in the lab.
He isolated the Penicillium notatum and demonstrated that it had antimicrobial effects on Gram-positive pathogens (that cause scarlet fever, pneumonia, gonorrhea, meningitis, diphteria)
Joseph Lister
Ernest Duchesne
Alexander Flemming

3. Earlier Antibiotic “Research”
Microbes have produced antibiotics for a number of reasons:
As anticompetitor compounds (e.g., bacteriocins)
As predatory compounds (e.g., lysing enzymes)
As quorum-sensing molecules (e.g., nisin)
Such antibiotics can affect many other species (broad spectrum) or affect only a few species (narrow spectrum).
Most of our antibiotics are derivatives of natural microbial products.
We are taking an ancient form of chemical warfare with the human body as the battlefield.

4. The Problem of Drug Resistance
Lecture Outline
Antibiotics & resistance
Costs: Reversion & Compensation
Antibiotics & Adaptive Landscapes
Predicting Resistance
Summary

5. The Problem of Drug Resistance
Lecture Outline
Antibiotics & resistance
Costs: Reversion & Compensation
Antibiotics & Adaptive Landscapes
Predicting Resistance
Summary

6. Resistance in the Intensive Care Unit National Nosocomial Infections Surveillance System Report, 2003
Klebsiella pneumoniae
Pseudomonas aeruginosa
10 %
23 %
28 %
52 %
Enterococcus sp.
Staphylococcus aureus

7. Resistance to Resistance is Futile
With sustained use of an antibiotic, resistant strains appear and spread.
As a new antibiotic is introduced, the evolutionary challenge is on– and microbes generally answer this challenge.
Facilitating spread is the appearance of resistance genes on mobile genetic elements, such as plasmids (this can lead to transfer between species).
Often resistance is found in commensal bacteria, which in some cases can serve as a genetic reservoir for pathogenic species.
Particularly troubling is the generation of multi-drug resistant strains of pathogenic bacteria.

8. Hospital-acquired infection
Resistance Matters
Causes of death, USA
Cause
Deaths/year
HIV/AIDS
18,000
Influenza
37,000
Breast cancer
40,000
Hospital-acquired infection
90,000
In human disease, drug-resistant bacteria can lead to:
Increased risk of mortality
Increased length of hospital stay
Increased use of other drugs (which can be expensive and lead to complications)
Foci for the spread of all these problems to other patients
Thus, drug-resistance causes large financial and health costs.

9. The Problem of Drug Resistance
Lecture Outline
Antibiotics & resistance
Costs: Reversion & Compensation
Antibiotics & Adaptive Landscapes
Predicting Resistance
Summary

10. Case Study: Tuberculosis
Tuberculosis is an infectious disease caused by the bacterium Mycobacterium tuberculosis. The disease generally progresses in the lungs causing tissue destruction and necrosis.
The global incidence of TB has been on the rise, reaching highest densities in Africa, the Middle East and Asia.
Particularly alarming is the rise of drug-resistant and multi-drug resistant strains of TB (e.g., bacteria resistant to isoniazid and rifampicin).
Mycobacterium
tuberculosis
TB infection
The origin of resistance in TB will be affected by the intensity of antibiotic usage in a given region.
The maintenance of resistance will depend, in part, on its fitness effects and evolutionary options of various TB strains.
0.3%
0.2%
0.1%
0.05%

11. Resistance in Tuberculosis
Gagneux, Davis Long and colleagues explored the fitness cost of drug resistance in TB in vitro.
From a fully grown culture of a clinical isolate, they exposed the bacteria to the antibiotic rifampicin.
Resistant colonies were isolated and genotyped (generally single base changes in the rpoB gene)
These authors wanted to gauge the costs (if any) of antibiotic resistance.
The antibiotic resistant strain and antibiotic-sensitive ancestor were placed (at roughly equal starting frequency) in a liquid broth and competed for a growth period (one month!)
With knowledge about the starting densities and final densities of the resistant strain (R) and the ancestor (A), a relative fitness measure can be computed:
Sebastien
Gagneux
Clara Davis
Long
The Competition
vs.

12. Costs of Resistance
Gagneux et al. find that rifampicin resistance is costly in TB.
Furthermore, they find that the cost of resistance can depend on the genetic background of the strain.
Such costs have been found in many other cases:
Streptomycin resistance in E. coli
Fusidic acid resistance in S. aureus
Fusidic acid resistance in S. typhimurium
Colicin resistance in E. coli
Rifampicin resistance in E. coli
Most of these costs were measured in vitro. But…
By looking in paired patient isolates, these authors found that resistance was often costly, but not always… ? =

13. Reversion and Compensation
Schrag, Perrot & Levin (1997) selected for streptomycin-resistant bacteria.
These authors then evolved this bacteria for 180 generations in the absence of streptomycin.
The initial resistant mutant was costly.
However, the streptomycin resistant strain did not revert to sensitivity. It remained resistant to strep.
Further, the initial cost of streptomycin resistance was ameliorated– there was compensation.
They discovered second site mutations (in rpsL).
Through genetic manipulation, they constructed all combinations of base changes and found a rugged landscape!
24 hrs.
24 hrs.
resistant
sensitive
compensated
uncompensated
fitness
Take 5 minutes to talk about the following:
Why do you think this landscape is rugged? If landscapes of
pathogenic bacteria generally resemble the one found by
Schrag et al., how would that affect your decision about
length and strength of antibiotic treatments?

14. Eyeing the Landscape Let’s extend the landscape metaphor further:
sensitive
wild-type
Let’s extend the landscape metaphor further:
Imagine that in the absence of the antibiotic, the population (mostly) resides on a sensitive wild-type peak.
Then an antibiotic is applied.
The sensitive peak drops out.
Any resistant mutants are immediately selected– now the population (mostly) resides on a resistant peak.
Assume that the antibiotic is removed.
Now, the sensitive peak reappears.
If there are many ways to compensate, then the evolutionary trajectory can take several possible paths.
It is possible that reversion occurs; it is possible compensation occurs.
Some relevant questions:
Is the landscape actually rugged?
Are compensatory peaks higher or lower than reversion peaks?
How many ways are there to become resistant? How many ways to compensate?
antibiotic
absent
resistant-types
sensitive
wild-type
antibiotic
present
resistant-types
sensitive
wild-type ? ? ? ?
compensated
resistants

15. The Problem of Drug Resistance
Lecture Outline
Antibiotics & resistance
Costs: Reversion & Compensation
Antibiotics & Adaptive Landscapes
Predicting Resistance
Summary

16. Evolution Done Wright Wright’s Shifting Balance Theory
Two basic assumptions:
Some genetic epistasis leading to distinct “peaks” in the landscape
A metapopulation of semi-isolated sparsely populated subpopulations
Migration is low between subpopulations, but present
Genetic drift occurs within demes
Phase 1: Subpopulations drift over the adaptive landscape
Phase 2: Selection drives subpopulations to new peaks
Phase 3: Competition between subpopulations where the most fit “pulls” the metapopulation onto its adaptive peak
“[Fisher’s] theory is one of complete and direct control by natural selection while I attribute greatest immediate importance to the effects of incomplete isolation”
(Wright, 1931 as quoted in Provine, 1986)

17. Resistance in “the Balance”
Phase 1: Demes drift over the adaptive landscape
Phase 2: Selection drives demes to new peaks
Phase 3: Interdemic competition where the most fit deme “pulls” the metapopulation onto its adaptive peak
new environment changes landscape
subpopulation 1
subpopulation 4
subpopulation 2
subpopulation 5
subpopulation 3
subpopulation 6
Population structure allows a more thorough exploration of the adaptive landscape and
thus the ascent of a higher peak globally.

18. Extending the Metaphor

19. The Role of Population Structure

20. Simulating Population Structure
The population need not be structured into discrete subpopulations for the discovery of higher peaks.
Imagine that individual genotypes live on a lattice and can reproduce with either global or local dispersal.
If the adaptive landscape is rugged and the population starts off in a valley, then the following predictions can be made:
Under global dispersal, a new mutation that improves fitness quickly takes over the population. It is likely that this selective sweep moves the population to a sub-optimal peak.
Under local dispersal, a new mutation that improves fitness slowly spreads through the population. It is possible that an even better mutation may be discovered during this selective creep.
Local Neighborhood
Global Neighborhood
Low fitness
High fitness
time
average fitness
local
global

21. Antibiotics as a Test Case
“It would clearly be desirable…to conduct selection experiments in subdivided and mass populations, making sure that each selection regime is replicated so that any treatment effects can be discerned.” (Coyne et al., 1997)
1) Obtain several rifampicin resistant strains in E. coli
2) Place each strain in an environment with structure (local dispersal) and no structure (global dispersal) without rifampicin
3) Track the average fitness in each treatment …
Media
+ Rifampicin
calculate
fitness

22. Slow and Steady Wins the Race
Theoretical Predictions:
time
average fitness
global
local
Lab Results: * * * *
Hypothesis:
A structured antibiotic resistant population is more likely to find higher fitness mutations
(be they reversions or compensations)

23. Not a Creature Was “Stirring…”
Another group of researchers allowed antibiotic-resistant bacteria to evolve over many generations in both a flask (in vitro) and in a mouse (in vivo). Their results were the following:
Organism
Antibiotic
Fraction of Replicates Reverting (Flask)
Fraction of Replicates Reverting (Rat/Mouse)
Reference
Staphylococcus aureus
Fusidic acid
4/28
(14%)
4/12
(33%)
Nagaev et al., 2001
Salmonella typhimurium
2/28
(7%)
14/25
(56%)
Björkman et al., 2000 
Take 2 minutes to talk about the following:
Propose some alternative hypotheses to explain the
differences between the rates of reversion in vitro versus
in vivo. How would you experimentally distinguish your
hypotheses? ? 

24. The Problem of Drug Resistance
Lecture Outline
Antibiotics & resistance
Costs: Reversion & Compensation
Antibiotics & Adaptive Landscapes
Predicting Resistance
Summary

25. antibiotic concentration
Predicting Evolution
Miriam Barlow and Barry Hall are developing a technique to predict the likely paths of antibiotic resistance.
They start with a gene that currently does not confer high resistance (e.g., TEM-1 b lactamase does not hydrolyze modern cephalosporins).
Next, they produce many mutant versions of the gene through error prone PCR.
Then, they introduce these mutant genes into bacterial cells.
Next, they grow up this population of mutants in a gradient of antibiotic and select cells from the highest drug concentration.
They isolate the resistance gene and begin the process anew. After a few rounds of this cycle, they sometimes have a gene that confers high levels of resistance.
Miriam
Barlow
Error
prone
PCR
Introduce
mutations
into cells
antibiotic concentration
Select
cells

26. The Barlow-Hall Method
How well does this in vitro method predict natural antibiotic resistant mutations?
Using this method on TEM alleles, the four most common amino acid substitutions found in vitro (E104K, R164S, G238S, and E240K) were also the four most common amino acid substitutions found in naturally occurring extended-spectrum TEM alleles.
How does knowledge of the likely evolutionary paths help us?
As we design new drugs, knowing the mutations likely to generate resistance may help us discern tradeoffs between resistance to different drugs.
That is, we may be able to map those regions of genome space (a HUGE space) that engender resistance to drug A and those regions that engender resistance to drug B. If these regions do not overlap, then we may be able to “trap” our bacterium by simultaneous drug use.
Some evidence of tradeoffs between cefepime and cefuroxime.
types
resistant
to drug A
types
resistant
to drug B

27. The Problem of Drug Resistance
Lecture Outline
Antibiotics & resistance
Costs: Reversion & Compensation
Antibiotics & Adaptive Landscapes
Predicting Resistance
Summary

28. Summary
Antibiotic resistance is a serious public health problem. Multi-drug resistance is particularly worrying.
Understanding the ecological and evolutionary consequences of resistance (e.g., fitness costs, probability of reversion versus compensation) can actually inform epidemiological thinking (e.g., in the case of TB).
Antibiotic resistance also serves as an ideal testing ground to explore critical issues within evolutionary biology (issues that concerned Wright and Fisher), including the shape of adaptive landscapes, the role of population structure, and the constraints on evolutionary trajectories.
One exciting area (from both practical and academic perspectives) is the prospect of predicting specific evolutionary trajectories engendering drug resistance and using this information to design more effective treatment.