Representative publications: Lazar et al MSB 2013, Lazar et al. Nature Communications 2014 (Appendix)
Understanding how evolution of microbial resistance towards a given antibiotic
enhance (cross-resistance) or decrease (collateral sensitivity) fitness in the presence
of other drugs is a challenge of profound importance for several fields of basic and
applied research
84. Despite its obvious clinical importance, our knowledge is still
limited, not least because this problem has been addressed largely by small-scale
clinical studies. By combining laboratory evolution (Figure 16A), genome sequencing,
and functional analyses (Figure 16B), recent works charted the maps of
cross-resistance/collateral sensitivity interactions between antibiotics in E. coli
85-87, and
explored the mechanisms driving these evolutionary patterns
86.
Figure 16A. In prior works
85,86, we initiated laboratory evolutionary experiments
starting with a single clone of E. coli K12. Parallel evolving bacterial populations were
exposed to gradually increasing concentrations of one of 12 clinically relevant
antibiotics, leading to up to 328-fold increase in the minimum inhibitory
concentrations (MICs) relative to the wild-type. In all cases, the resistance levels
were equal to or above the EUCAST clinical break-points. 52% of the evolved strains
showed resistance to multiple antibiotics. Adapted from Pal et al. 2015
29.
Figure 16B. The laboratory evolved lineages were subjected to in-depth phenotypic and genomic analysis with the aim to explore the accompanying changes in drug sensitivity and the underlying molecular mechanisms thereof. Adapted from Pal et al.
2015
29.
The exceptionally large scale of these works allowed to derive several conceptually
novel conclusions
29. First, antibiotic cross-resistance is frequent and computationally
predictable by integrating the accumulated knowledge on functional and chemical
antibiotic properties
85. Second, mutations that cause multi-drug resistance
simultaneously enhance sensitivity to many other drugs
86,87. Third, these works
offered an insight into the mechanisms underlying collateral sensitivity
86. In this short
chapter, we summarize recent advances in this emerging research area. We
highlight the potential and limitations of current approaches, review the underlying
molecular mechanisms of these phenomena, and suggest new research directions
for future studies. Specifically, we discuss how these advances could be exploited for the development of novel antimicrobial strategies.
Multi-drug resistance emerges in response to evolution against a single drug To chart the map of cross-resistance, recent works initiated parallel laboratory evolutionary experiments to adapt to increasing dosages of one of 12 clinically relevant antibiotics (Table 6).
Table 6. Antibiotics employed in the study by Lazar and colleagues and the corresponding modes of action.
Evolved populations reached up to 300-fold increas in the minimum inhibitory concentrations relative to the ancestor
85-87. As a next step, the corresponding changes in susceptibilities of the lab-evolved populations were measured against a panel of other antibiotics, allowing us to infer a network of cross-resistance interactions (Figure 17). Laboratory-evolved lines were subjected to whole-genome sequence analysis and biochemical assays to decipher the underlying molecular mechanisms of these interactions. These studies revealed that:
a) The cross-resistance network is dense, indicating that exposure to a single
antibiotic frequently yields multidrug resistance.
b) The populations frequently evolve asymmetric cross protection, where stress A protects against stress B but not vice versa.
c) The network of cross-resistance is predictable based on antibiotic properties.
d) Finally, laboratory studies recapitulated major patterns of antibiotic cross-resistance observed in the clinics.
Figure 17. Based on the high-throughput measurement of antibiotic susceptibilities in laboratoryevolved bacteria, two networks can be deciphered. An arrow from antibiotic A to B indicates that evolution of resistance to A generally increases (collateral sensitivity) or decreases (cross-resistance) susceptibility to B. Adapted from Pal et al.
2015.
These works also identified a strong signature of parallel evolution at the molecular
level that emerged across populations adapted to different antibiotics, and such
parallel mutations delivered resistance to multiple antimicrobial agents
29,85. The
molecular mechanisms underlying antibiotic cross-resistance appeared to be very diverse, including mutations in multi drug efflux pumps, metabolic genes, and genes involved in bacterial defense against c) oxidative, d) nutritional and e) membrane stresses. These works also suggested that genome-wide transcriptional rewiring mediated by global transcriptional regulatory genes has an important contribution to cross-resistance patterns.
Perhaps the most remarkable aspect of these findings is that cross-resistance is delivered by mutations with wide pleiotropic effects
85,86. Therefore, cross-protection may be more general
88, and opens the possibility that stressful conditions unrelated to antibiotic pressure may, as a byproduct, select for enhanced antibiotic tolerance in nature.
Evolution of multi-drug resistance promotes hypersensitivity to certain drugs
The phenomenon
Prior studies demonstrated that evolution of resistance to a single antibiotic is frequently accompanied by increased resistance to multiple other antimicrobial agent
29. However, very little is known about the occurrence of collateral sensitivity (i.e. when evolution of resistance yields enhanced sensitivity to other antibiotics).
Given the prevalence of resistance conferring mutations with pleiotropic effects, researchers speculated that such collateral sensitivity interactions could frequently emerge. Large-scale laboratory evolution studies demonstrated that it was indeed so.
Strikingly, not only cross-resistance, but also collateral sensitivity interactions
frequently occur during evolution of antibiotic resistance (Figures 17 and 18).
Figure 18.. An example of collateral sensitivity. Dose response curve of the wild-type control and a tobramycin (aminoglycoside) resistant bacterial strain (TOB3). TOB3 shows resistance to tobramycin, but surprisingly, it has elevated susceptibility to a drug with unrelated mode of action (gyrase inhibitor, nalidixic acid). Adapted from Lazar et al. 2013.
The mechanisms
Understanding the mechanisms underlying collateral sensitivity interactions is still at
an embryonic stage. We mention one example here: resistance mechanisms to
various antibiotics via alteration of membrane potential have been reported in both
laboratory studies and clinical settings, and such changes underlie the
hypersensitivity of bacteria to other antibiotics
86. These results indicate the existence of antagonistic mechanisms by which bacteria modulate intracellular antibiotic concentration through altering membrane polarity
86(Figure 19).
Figure 19. A mechanism underlying collateral sensitivity. Altering the membrane potential across the inner bacterial membrane has two opposing effects: it reduces the uptake of many aminoglycoside-related antibiotics but simultaneously leads to the reduced activity of PMF-dependent efflux pumps. Adapted from Lazar et al. 2013.
Development of novel multi-drug therapies
The experimental map of cross-resistance/collateral sensitivity could serve as a
unique resource and potentially permit informed decisions in medicin
29. For example,
the choice of optimal antibiotic combinations depends on both the presence of
to both drugs simultaneously. It has been shown that cross-resistance between two
antibiotics is largely independent of whether they show synergistic effects in
combination
89. Combination of large-scale information on antibiotic synergism and
cross-resistance could be especially informative for future development of multidrug
therapies. For example, it remains controversial whether temporal rotation of
antibiotics could select against the development of resistance
30. These works
strongly indicate that the success of such a strategy depends on the choice of
antibiotics: treatment with a single antibiotic and then switching to a cross-sensitive
partner may be a viable strategy. An alternative approach relies on the simultaneous
administration of two agents in collateral sensitivity interaction to inhibit both the
wild-type and the resistant subpopulations, and thereby prevent the emergence of
resistance
30,89(Figure 20).
Figure 20. Potential applications of collateral sensitivity to eradicate antibiotic resistant bacteria. Antibiotic pairs showing collateral sensitivity could be administered simultaneously as drug combination (a) or in an alternating fashion (b). Abbreviation:
WT, wild type. See Pal et al. 2015 for more details.
Testing the long-term efficacy of novel therapeutic agents
By analyzing the maps of cross-resistance, researchers unveiled some general principles governing the evolution of cross-resistance patterns. By integrating available data on antibiotic properties, it has been shown that cross-resistance is partly predictable
85. These results pave the way towards in silico methods to estimate the cross-resistance propensity of novel antimicrobial compounds before entry into clinical usage. At least five key issues need to be investigated in more depth by future studies:
1) Evolutionary conservation of cross-resistance maps and the underlying molecular mechanisms across bacterial species.
2) Exploiting the fitness costs of plasmid mediated antibiotic resistance mechanisms.
3) Confirmation of laboratory results with in vivo and clinical studies. Indeed, comparison of existing large-scale clinical data on multidrug resistance with results of laboratory evolution studies has a central importance.
4) Integrating information from metagenomic approaches that aim to identify resistance genes from environmental reservoirs.
5) Establishing how the evolvability of further resistance is influenced by
cross-resistance and collateral sensitivity interactions.
In document
Evolution and systems biology
(Pldal 36-46)