Final Report Summary - RARE (Revealing Antibiotic Resistance Evolution)
RARE (Revealing Antibiotic Resistance Evolution) was a project that aimed to find novel ways of using antibiotics more efficiently in order to stop or slow down evolution of antibiotic resistance. For this goal, we proposed to use long-term evolution experiments and next generation sequencing to find the genetic pathways leading to antibiotic resistance. The proposed project is partially completed since the principle investigator of this project has accepted a position at the University of Texas Southwestern Medical Centre and moved to United States 30 months after the project was started. Although the project has not been completed, a great deal of work has been done and the findings so far suggest that finding novel approaches to use antimicrobials more efficiently is indeed possible.
Benefiting from evolutionary trade-offs for fighting against resistance.
Bacteria develop resistance against inhibitory drugs. This can happen either by horizontal gene transfer where bacteria acquire resistance-conferring genes from other organisms or spontaneous mutations that confer resistance. In our group, we mostly focus on emergence of spontaneous mutations by performing long-term evolution experiments. For these experiments, we use Morbidostat, a device that we developed for studying evolution of bacterial drug resistance [1, 2]. Morbidostat maintains a nearly constant drug-induced selection pressure on evolving populations as bacterial populations acquire higher resistance. In order to understand how bacteria evolve resistance in complex environments such as drug combinations, we tried to gather available experimental information about evolution of bacteria against clinically relevant drugs. However, we quickly realized that it was not an easy task since many experiment were carried out under different conditions and backgrounds. Therefore, we decided to carry out an extensive initial experiment where evolved wild type drug sensitive Escherichia coli (MG1655, K12) against 22 different drugs using strong and mild selection settings. In these experiments, we grew E. coli cells in drug gradients and propagated populations that survived in the highest drug concentration or one fourth of highest drug concentration in strong selection and mild selection respectively. At the end of the evolution experiments, we picked a representative clone from each culture and investigated genetic changes responsible for resistance. Furthermore, these clones were carefully phenotyped for their resistance against all of the drugs we used in our evolution experiments. This work was published in Molecular Biology and Evolution in July 2014 [3].
There were three major finding in this work and all these findings became very instrumental in our current studies for finding novel strategies for better use of existing antibiotics. These outcomes are listed below in detail.
1. Cross-resistance is a common outcome of evolution of resistance and has to be seriously considered when designing therapies. We used 22 different drugs selected from seven different antibiotic classes in our evolution experiments. At the end of the evolution experiment, we selected on clone from each population and measured its resistance levels against all 22 drugs we used. In many cases, these clones were resistant against many other drugs even though they were never exposed to these drugs. For examples, bacterial strains that evolved against DNA gyrase inhibitors had considerably high levels of cross-resistance against beta lactams and protein synthesis inhibitors. By combining the cross-resistance measurements with the whole genome sequencing data of the evolved strains, we were able to make an extensive list of mutations responsible for resistance and cross-resistance. By using this valuable data set, we and other researchers can now predict outcomes of evolution experiments and design therapies to minimize the rate of evolution of resistance.
2. E. coli becomes hypersensitive to antibiotics when they evolve resistance against aminoglycosides. When we quantified the cross-resistance of evolved strains, we realized that strains which became resistant against aminoglycosides developed hypersensitivity against almost all other antibiotic classes. This phenotype was mainly due to a point mutation in the TrkH gene, which codes for a potassium transporter. This finding was confirmed by two back-to-back publications from other groups in Europe [4, 5].
3. Strong selection promotes cross-resistance. When we compared cross-resistance phenotypes of strongly selected and mildly selected strains, we found that strongly selected strains had higher cross-resistance levels compared to mildly selected strains. According to our genotype data, this difference was largely due to higher number of pathway specific mutations found in strongly selected strains.
Based on these observations, we are now developing new strategies that can stop or slow down evolution of antibiotic resistance and testing these strategies using the Morbidostat. Although we could not complete the entire project because of early termination, we are still working on this project and planning to collaborate with medical doctors. We anticipate that these findings will encourage several researchers to revisit the important problem of antibiotic resistance.
1. Toprak, E., et al., Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat Genet, 2012. 44(1): p. 101-5.
2. Toprak, E., et al., Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat Protoc, 2013. 8(3): p. 555-67.
3. Oz, T., et al., Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol Biol Evol, 2014. 31(9): p. 2387-401.
4. Lazar, V., et al., Bacterial evolution of antibiotic hypersensitivity. Mol Syst Biol, 2013. 9: p. 700.
5. Imamovic, L. and M.O. Sommer, Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci Transl Med, 2013. 5(204): p. 204ra132.
Benefiting from evolutionary trade-offs for fighting against resistance.
Bacteria develop resistance against inhibitory drugs. This can happen either by horizontal gene transfer where bacteria acquire resistance-conferring genes from other organisms or spontaneous mutations that confer resistance. In our group, we mostly focus on emergence of spontaneous mutations by performing long-term evolution experiments. For these experiments, we use Morbidostat, a device that we developed for studying evolution of bacterial drug resistance [1, 2]. Morbidostat maintains a nearly constant drug-induced selection pressure on evolving populations as bacterial populations acquire higher resistance. In order to understand how bacteria evolve resistance in complex environments such as drug combinations, we tried to gather available experimental information about evolution of bacteria against clinically relevant drugs. However, we quickly realized that it was not an easy task since many experiment were carried out under different conditions and backgrounds. Therefore, we decided to carry out an extensive initial experiment where evolved wild type drug sensitive Escherichia coli (MG1655, K12) against 22 different drugs using strong and mild selection settings. In these experiments, we grew E. coli cells in drug gradients and propagated populations that survived in the highest drug concentration or one fourth of highest drug concentration in strong selection and mild selection respectively. At the end of the evolution experiments, we picked a representative clone from each culture and investigated genetic changes responsible for resistance. Furthermore, these clones were carefully phenotyped for their resistance against all of the drugs we used in our evolution experiments. This work was published in Molecular Biology and Evolution in July 2014 [3].
There were three major finding in this work and all these findings became very instrumental in our current studies for finding novel strategies for better use of existing antibiotics. These outcomes are listed below in detail.
1. Cross-resistance is a common outcome of evolution of resistance and has to be seriously considered when designing therapies. We used 22 different drugs selected from seven different antibiotic classes in our evolution experiments. At the end of the evolution experiment, we selected on clone from each population and measured its resistance levels against all 22 drugs we used. In many cases, these clones were resistant against many other drugs even though they were never exposed to these drugs. For examples, bacterial strains that evolved against DNA gyrase inhibitors had considerably high levels of cross-resistance against beta lactams and protein synthesis inhibitors. By combining the cross-resistance measurements with the whole genome sequencing data of the evolved strains, we were able to make an extensive list of mutations responsible for resistance and cross-resistance. By using this valuable data set, we and other researchers can now predict outcomes of evolution experiments and design therapies to minimize the rate of evolution of resistance.
2. E. coli becomes hypersensitive to antibiotics when they evolve resistance against aminoglycosides. When we quantified the cross-resistance of evolved strains, we realized that strains which became resistant against aminoglycosides developed hypersensitivity against almost all other antibiotic classes. This phenotype was mainly due to a point mutation in the TrkH gene, which codes for a potassium transporter. This finding was confirmed by two back-to-back publications from other groups in Europe [4, 5].
3. Strong selection promotes cross-resistance. When we compared cross-resistance phenotypes of strongly selected and mildly selected strains, we found that strongly selected strains had higher cross-resistance levels compared to mildly selected strains. According to our genotype data, this difference was largely due to higher number of pathway specific mutations found in strongly selected strains.
Based on these observations, we are now developing new strategies that can stop or slow down evolution of antibiotic resistance and testing these strategies using the Morbidostat. Although we could not complete the entire project because of early termination, we are still working on this project and planning to collaborate with medical doctors. We anticipate that these findings will encourage several researchers to revisit the important problem of antibiotic resistance.
1. Toprak, E., et al., Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat Genet, 2012. 44(1): p. 101-5.
2. Toprak, E., et al., Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat Protoc, 2013. 8(3): p. 555-67.
3. Oz, T., et al., Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol Biol Evol, 2014. 31(9): p. 2387-401.
4. Lazar, V., et al., Bacterial evolution of antibiotic hypersensitivity. Mol Syst Biol, 2013. 9: p. 700.
5. Imamovic, L. and M.O. Sommer, Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci Transl Med, 2013. 5(204): p. 204ra132.