We all have heard of a saying, “That which doesn’t kill us makes us stronger” by Friedrich Nietzsche. This is especially true in case of bacteria, organisms that are found everywhere ranging from “on us” to “inside us”. these microscopic organisms are responsible for many of the most deadliest diseases to humankind. Advent of antibiotics provided relief as they were considered to be the best solution to the diseases caused by bacteria. however, on a longer run it was established that these organisms indeed develop resistance to the antibiotics making them invulnerable to the effects of antibiotics and making them more stronger in the process.
This antibiotic resistance is the result of mutations that takes place in a bacterial population exposed to a specific drug leading to a evolved population which becomes impenetrable by the drug. The infections that are caused by these newly evolved strains of bacteria become very challenging to cure emanating longer stays in hospitals. This very evolution however, has opened channels to explore for solutions to the issue of antibiotic resistance as the phenomena of evolution which causes to build resistance against one type of drug might also result in developing hypersensitivity to others, thereby preventing multi-drug resistance. This strategy is termed as collateral sensitivity, which is a two way trade-off amounting to increased resistance to one drug and causing increased sensitivity to the other drug (Szybalski & Bryson, 1952).
Barbosa, Roemhild et al. attempted to exploit this aspect to decipher that evolving collateral sensitivity will ultimately slowdown the evolution of resistance by combination and therapies that are sequenced (Rodriguez De Evgrafov et al., 2015). Their biggest challenge was to determine the stability of their system such that at one point the bacterial population goes extinct or at least render them invulnerable to develop multi-drug resistance. In order to test their system they incorporated the bacterium Pseudomonas aeruginosa as it is believed to develop collateral sensitivity to to different drug treatments
They subjected Pseudomonas aeruginosa to a two-step evolution, they utilized already evolved extremely resistant populations of P. aeruginosa, procured by serial passage experiments having increased concentrations of bactericidal antibiotics which were clinically relevant. They tested collateral sensitivity which would reciprocate i.e. the first target drug would create resistance while the second drug would show impend hypersensitivity on in first set-up and vice-versa. They also treated the bacteria by switching the antibiotics to collateral sensitivity but the administration of first drug remained continued along with the administration of the second drug hence they confirmed a constraint environment. Altogether, they laid down a total of four conditions which were running parallelly i.e. minimal or maximal increase of second drug with and without the presence of first drug. Simultaneously run control experiments i.e. without any antibiotic ensured treatment success. incorporation of quantification of extinct population frequency, absorbance measurements and characterization of changes amounting to antibiotic resistance of the evolving bacteria in comparison to what was previously observed in Pseudomonas aeruginosa further ensured the proofing of the experiment.
Authors conducted experiment to test stability in the evolution of reciprocal/reverse collateral sensitivity by exposing the clones from previous resistant populations with new set of antibiotics at high concentrations which resulted hypersensitivity in resistant populations. they performed a series of evolution experiments for 12 days following serial transfer protocol with beginning population size of approximately 106 CFU/ml. They evaluated each population in eight replicates and 5 treatment groups i.e.:
- Controls (without anti-biotic)
- Low level of increasing concentration of antibiotic (Unconstrained Evolution)
- High level of increasing concentration of antibiotic (Unconstrained Evolution)
- Low level of increasing concentration of antibiotic (Constrained Evolution)
- High level of increasing concentration of antibiotic (Constrained Evolution)
The authors also validated their findings by conducting repetitive evolution experiments by incorporating resistant population as the initial material. they used approximately 107 cells in contrast to a single clone, but reduced the treatments. In total they utilised 38 resistant populations.
- (PIT) Piperacillin/tazobactam and (STR) streptomycin
- (CAR) Carbenicillin and (GEN) Gentamicin
The authors in their experiment found that it is highly circumstantial that collateral sensitivity could be exploited for sequence based treatments as it validity is dependent on the combination of drugs used and also the order in which they are used, they also found that epistatic genetic interactions also play a role in their selection. The increased extinction rates determines that bacterial adaptation was constrained in treatments when a switch to ß-lactam was made. This effect was maximised when the second drug was administered in constraint environment. Their findings opened doors to the possibilities of further dwelling into deeper research of an unexplored strategy of treatment i.e. using single drug therapy after being treated with combination treatment. in this manner, the evolutionary trade-off of drug sensitivity could be maximised.
To be specific, the drug pair of CAR/GEN after deep analysis showed high extinction in comparison to growth improvements when given strong dose administration over mild dose. It is interesting to see how rate of extinction are often ignored and left unreported as a part of evolutionary outcome in corresponding studies (Yen & Papin, 2017). The authors are convinced upon the fact that, since antibiotic therapy is aimed towards attempt to eliminate bacterial pathogens, the extinction frequencies from dated experiments on evolution have shown variance depending upon different types of treatment and hence these deliberations could serve in order to refine the understanding of efficacy of the treatment.
In their experiment on CAR/GEN pair at clonal level demonstrated stability in collateral sensitivity as a result of slow adaptation and efficiency of re-sensitization. however, it was the negative epistasis of drug-specificity that re-sensitization was seen during switch from aminoglycoside to a ß-lactam. On the contrary the reciprocal collateral sensitivity test showed less stability as evident from low levels of extinction and absence of re-sensitisation. Few outcomes of their findings were unexplained with their current data set and the high instability could not be interpreted. Their work has drawn certainly drawn attention towards the need of careful evaluation of novel options of treatment.
Evolution is unique and inevitable, and so is the development of new methodologies to tackle the issue of drug-resistance. With many factors to play a role in achieving success to slow down the rate of drug-resistance or to completely eliminate the pathogenic bacteria, evolution amounting to collateral sensitivity has drawn attention towards designing a sustainable and stable approach of drug therapy to control infection in coalescence with traditional and other newly developed techniques.
Barbosa, C., Romhild, R., Rosenstiel, P., & Schulenburg, H. (2019, Oct 29). Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa. Elife, 8. https://doi.org/10.7554/eLife.51481
Rodriguez De Evgrafov, M., Gumpert, H., Munck, C., Thomsen, T. T., & Sommer, M. O. A. (2015). Collateral Resistance and Sensitivity Modulate Evolution of High-Level Resistance to Drug Combination Treatment in Staphylococcus aureus. Molecular Biology and Evolution, 32(5), 1175-1185. https://doi.org/10.1093/molbev/msv006
Szybalski, W., & Bryson, V. (1952). Genetic studies on microbial cross resistance to toxic agents. I. Cross resistance of Escherichia coli to fifteen antibiotics. Journal of bacteriology, 64(4), 489-499. https://doi.org/10.1128/JB.64.4.489-499.1952
Yen, P., & Papin, J. A. (2017). History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment. PLOS Biology, 15(8), e2001586. https://doi.org/10.1371/journal.pbio.2001586