The Problem of Drug Resistance

1. The Problem of Drug Resistance E3: Lecture 11

2. Early Antibiotic Research • Joseph Lister (1827-1912) • - 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 (1874-1912) • In 1897, he demonstrated that E. coli was killed when cultured with Penicilliumglaucum. • He also showed that injection of this mold into animals infected with typhoid bacilli prevented the advent of the disease. • Alexander Flemming (1881-1955) • 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 UnitNational Nosocomial Infections Surveillance System Report, 2003 Klebsiella pneumoniae Pseudomonas aeruginosa 23 % 10 % 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. Resistance Matters Causes of death, USA • 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 Mycobacterium tuberculosis TB infection 0.3% 0.2% 0.1% 0.05% • 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). • 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.

11. Resistance in Tuberculosis Sebastien Gagneux Clara Davis Long vs. The Competition • 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:

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 fitness resistant sensitive uncompensated compensated • 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. 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 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 position. • 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. Wright’s Shifting Balance Theory Evolution Done Wright • 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” new environment changes landscape subpopulation 1 subpopulation 2 subpopulation 3 subpopulation 4 subpopulation 5 subpopulation 6 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 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. Global Neighborhood High fitness Local Neighborhood Low fitness average fitness time 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 global

21. Antibiotics as a Test Case … calculate fitness … calculate fitness “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

22. local average fitness global time Slow and Steady Wins the Race Theoretical Predictions: 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:  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. Error prone PCR Miriam Barlow Introduce mutations into cells Select cells 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.

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.