Mehmet A. Orman

The antibiotic tolerances of bacterial persisters have been attributed to transient dormancy. While persisters have been observed to be growth inhibited prior to antibiotic exposure, we sought to determine whether such a trait was essential to the phenotype. Furthermore, we sought to provide direct experimental evidence of the persister metabolic state so as to determine whether the common assumption of metabolic inactivity was valid. Using fluorescence-activated cell sorting (FACS), a fluorescent indicator of cell division, a fluorescent measure of metabolic activity, and persistence assays, we found that bacteria that are rapidly growing prior to antibiotic exposure can give rise to persisters and that a lack of replication or low metabolic activity prior to antibiotic treatment simply increases the likelihood that a cell is a persister. Interestingly, a lack of significant growth or metabolic activity does not guarantee persistence, as the majority of even "dormant" subpopulations (>99%) were not persisters. These data suggest that persistence is far more complex than dormancy and point to additional characteristics needed to define the persister phenotype.

Bacterial persisters are phenotypic variants with extraordinary tolerances toward antibiotics. Persister survival has been attributed to inhibition of essential cell functions during antibiotic stress, followed by reversal of the process and resumption of growth upon removal of the antibiotic. Metabolism plays a critical role in this process, since it participates in the entry, maintenance, and exit from the persister phenotype. Here, we review the experimental evidence that demonstrates the importance of metabolism to persistence, highlight the successes and potential of targeting metabolism in the search for anti-persister therapies, and discuss the current methods and challenges to understand persister physiology.

Bacterial persisters are rare phenotypic variants that temporarily tolerate high antibiotic concentrations. Persisters have been hypothesized to underlie the recalcitrance of biofilm infections, and strategies to eliminate these cells have the potential to improve treatment outcomes for many hospital-treated infections. Here we investigate the role of stationary phase metabolism in generation of type I persisters in Escherichia coli, which are those that are formed by passage through stationary phase. We find that persisters are unlikely to derive from bacteria with low redox activity, and that inhibition of respiration during stationary phase reduces persister levels by up to ∼1,000-fold. Loss of stationary phase respiratory activity prevents digestion of endogenous proteins and RNA, which yields bacteria that are more capable of translation, replication and concomitantly cell death when exposed to antibiotics. These findings establish bacterial respiration as a prime target for reducing the number of persisters formed in nutrient-depleted, non-growing populations.

Bacterial persisters exhibit an extraordinary tolerance to antibiotics that is dependent on their metabolic state. Although persister metabolism promises to be a rich source of antipersister strategies, there is relatively little known about the metabolism of these rare and transient phenotypic variants. To address this knowledge gap, we explored the use of several techniques, and we found that only one measured persister metabolism. This assay was based on the phenomenon of metabolite-enabled aminoglycoside killing of persisters, and we used it to characterize the metabolic heterogeneity of different persister populations. From these investigations, we determined that glycerol and glucose are the most ubiquitously used carbon sources by various types of Escherichia coli persisters, suggesting that these metabolites might prove beneficial to deliver in conjunction with aminoglycosides for the treatment of chronic and recurrent infections. In addition, we demonstrated that the persister metabolic assay developed here is amenable to high-throughput screening with the use of phenotype arrays.