Metabolic constraints on the evolution of antibiotic resistance

Abstract

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.

Antibiotic efficacy — context matters

“…Extended antibiotic exposure leads to genetically encoded resistance with collateral sensitivity or resistance to antibiotics of different classes [106], which may be exploited to potentiate population-level lethality by antibiotic cycling [107,108]. Recent efforts integrating metabolomic profiling with experimental evolution have revealed how bacterial metabolism may constrain the acquisition of antibiotic resistance and differential cross-resistance [109]. Mechanistic details explaining antibiotic synergy and antagonism remain nascent; future studies on multidrug efficacy and resistance would benefit from integrated exploration of environmental and physiological factors.…”

LC‐MS/MS‐based metabolic profiling of Escherichia coli under heterologous gene expression stress

“…19,20 There are reports on metabolic response of the drug resistant and susceptible bacteria to different drugs. [21][22][23][24] However, there is lack of detailed global information about the metabolic landscape of E. coli, a widely used bacteria, introduced with expression plasmids for recombinant protein production. In the present study, we have evaluated the metabolic changes in E. coli after the induction of ampicillin resistance by transformation of bacterial cells with pET21a (+) plasmid (antibiotic stress) and during the drainage of cellular It is suggested from our results that cells growing under stress may have reduced their vitamin K synthesis as a survival strategy, as the presence of vitamin K enhances the antibiotic drug activity widely by enhancing membrane permeabilization mechanism.…”

Modeling the growth of organisms validates a general relation between metabolic costs and natural selection

“…We now partially relax that assumption: we still keep R b the same, but allow the mutant death rated = − ln(p(t r + δt r ))/(t r + δt r ) to be different than d by some amount δd ≡d − d. For example if the survival probability p(t) was exponential for the wild-type, p(t) = exp(−ηt), and the rate η was modified in the mutant toη, then δd =η − η. One case where such considerations would come into play would be bacterial strains growing in an environment with antibiotics: a variant that acquired antibiotic resistance would have a lowered death rate, δd < 0, but also possibly non-trivial metabolic costs associated with maintaining the resistance [52][53][54], as discussed in more detail below. Since the mutant now has both altered birth ratẽ r = r + δr = ln(R b )/(t r + δt r ) and death rated, the population growth equation…”

“…lowering the death rate d in the example above) will be reflected in a significant positive fitness contribution s a > 0. But the molecular mechanisms that lead to resistance typically come with non-negligible metabolic costs (s c < 0) that slow bacterial growth in the absence of drug, for example over-expression of energy-consuming multi-drug efflux pumps [50], or switching to less energy-efficient biochemical pathways that are not targeted by the drug [52]. While s a may more than compensate for s c when the antibiotic is present, the resistant strains are at least initially at a disadvantage in environments without the antibiotic, where s c is likely the dominant contribution to s. Ref.…”

“…Given the large effective populations of bacteria, these values are large enough to put the mutant strains under strong selective pressure to reduce the metabolic price of maintaining resistance. Intriguingly the net result of this pressure under subsequent drug-free evolution is typically not the loss of the resistance, but rather additional mutations that compensate for the metabolic costs [52][53][54].…”