An Experimental Framework for Quantifying Bacterial Tolerance

Brauner, Asher; Shoresh, Noam; Fridman, Ofer; Balaban, Nathalie Q.

doi:10.1016/j.bpj.2017.05.014

Cited by 108 publications

(136 citation statements)

References 30 publications

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“…One reason for this is that, by definition, tolerant cells survive but do not grow in the presence of antibiotics (23, 36, 37), making it difficult to select tolerant bacteria. While many attempts have been made to develop methods to select tolerant cells in a heterogeneous population (11,(19)(20)(21), none of these can be used to routinely quantify the tolerance of a bacterial isolate in the clinic. Very recently, a method, called TDtest, which can easily detect antibiotic tolerance using a modified disk diffusion assay was described.…”

Section: Discussionmentioning

confidence: 99%

“…Such a method will subsequently enable the design of drugs to eradicate persistent infections. Several attempts have been made to solve the mysteries of antibiotic tolerance, particularly by isolating and quantifying tolerant variants from a heterogeneous population, yet none have been simple or cost-effective enough for use in clinics on a routine basis (11,(19)(20)(21).…”

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confidence: 99%

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Genetic and Transcriptomic Analyses of Ciprofloxacin-TolerantStaphylococcus aureusIsolated by the Replica Plating Tolerance Isolation System (REPTIS)

Matsuo

Hiramatsu

Singh

et al. 2019

Antimicrob Agents Chemother

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We developed a simple, efficient, and cost-effective method, named the replica plating tolerance isolation system (REPTIS), to detect the antibiotic tolerance potential of a bacterial strain. This method can also be used to quantify the antibiotic-tolerant subpopulation in a susceptible population. Using REPTIS, we isolated ciprofloxacin (CPFX)-tolerant mutants (mutants R2, R3, R5, and R6) carrying a total of 12 mutations in 12 different genes from methicillin-sensitive Staphylococcus aureus (MSSA) strain FDA209P. Each mutant carried multiple mutations, while few strains shared the same mutation. The R2 strain carried a nonsense mutation in the stress-mediating gene, relA. Additionally, two strains carried the same point mutation in the leuS gene, encoding leucyl-tRNA synthetase. Furthermore, RNA sequencing of the R strains showed a common upregulation of relA. Overall, transcriptome analysis showed downregulation of genes related to translation; carbohydrate, fat, and energy metabolism; nucleotide synthesis; and upregulation of amino acid biosynthesis and transportation genes in R2, R3, and R6, similar to the findings observed for the FDA209P strain treated with mupirocin (MUP0.03). However, R5 showed a unique transcription pattern that differed from that of MUP0.03. REPTIS is a unique and convenient method for quantifying the level of tolerance of a clinical isolate. Genomic and transcriptomic analyses of R strains demonstrated that CPFX tolerance in these S. aureus mutants occurs via at least two distinct mechanisms, one of which is similar to that which occurs with mupirocin treatment.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Genetic and Transcriptomic Analyses of Ciprofloxacin-TolerantStaphylococcus aureusIsolated by the Replica Plating Tolerance Isolation System (REPTIS)

Matsuo

Hiramatsu

Singh

et al. 2019

Antimicrob Agents Chemother

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“…In this study, we observed a novel form of antibiotic tolerance. Tolerance has been defined as increased time to killing 26 , as opposed to resistance (a change in the MIC), or persistence (the ability of a subpopulation of clonal bacteria to survive high concentrations of antibiotic 5 ). Tolerance to beta lactams such as ampicillin has been observed in E. coli cultures that exhibit slow growth or a long lag phase 5 , and E. coli mutants with longer lag phases can be selected through experimental evolution to match the time of treatment 27,28 .…”

Section: Discussionmentioning

confidence: 99%

Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance in a model gut microbiota

Aranda-Díaz

Obadia²,

Thomsen

et al. 2019

Preprint

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20Despite decades of investigation into how antibiotics affect isolated bacteria, it 21 remains highly challenging to predict consequences for communities in complex 22 55 or (iii) tolerance, whereby the entire population enters an altered physiological 56 state that is not susceptible to the antibiotic 5 . Members of multispecies 57 communities, such as biofilms and models of urinary tract infections, can display 58 altered sensitivity to antibiotics 6-9 . A few studies have delved into the molecular 59 mechanisms behind cross-species antibiotic protection and sensitization. For 60 5 example, the exoproducts of Pseudomonas aeruginosa affect the survival of 61 Staphylococcus aureus through changes in antibiotic uptake, cell-wall integrity, 62 and intracellular ATP pools 10 . In synthetic communities, intracellular antibiotic 63 degradation affords cross-species protection against chloramphenicol 11 . 64 Additionally, metabolic dependencies within synthetic communities can lower 65 the viability of bacteria when antibiotics eliminate providers of essential 66 metabolites, leading to an apparent change in the minimum inhibitory 67 concentration (MIC) of the dependent species 9 . However, we still lack 68 understanding of how contextual metabolic interactions between bacteria affect 69 the physiological processes targeted by antibiotics and the resulting balance 70 between growth inhibition (bacteriostatic activity) and death (bactericidal 71 activity). 72 73 Characterizing the impact of metabolic interactions on antibiotic susceptibility 74 requires functional understanding of how bacterial species interact during 75 normal growth. Interspecies interactions can occur through specific mechanisms 76 within members of a community (e.g. cross-feeding or competition for specific 77 resources), or through global environmental variables modified by bacterial 78 activity. An example of the latter is pH, which has recently been shown to drive 79 6 community dynamics in a highly defined laboratory system of decomposition 80 bacteria 12 . 81 82 While synthetic communities afford the opportunity to design and to tune 83 bacterial interactions, it is unclear whether findings are relevant to natural 84 communities. The stably associated gut microbiota of Drosophila melanogaster fruit 85 flies constitutes a naturally simple model community for determining how 86 metabolic interactions between species affect growth, physiology, and the action 87 of antibiotics 13 . This community consists of ~5 species predominantly from the 88Lactobacillus and Acetobacter genera 14 (Fig. 1a). Lactobacilli produce lactic acid 15 , 89 while acetobacters are acetic acid bacteria that are distinguished by their ability 90 to oxidize lactate to carbon dioxide and water 16 . Short chain fatty acids, such as 91 lactate, decrease the pH of natural fermentations and may constitute a 92 mechanism through which pH plays a prominent role in community dynamics. 93 The naturally low number of species in Drosophila gut microbiota and its 94 comp...

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“…High mortality rates and increasing health 3 care costs associated with fighting the bacterial infections call for designing new 4 effective therapeutic strategies [7,26]. A major challenge in overcoming treatment 5 failures is coming from ineffective eradication of antibiotic-susceptible 6 bacteria [12,29,34]. Despite the introduction and wide application of a very large range 7 of antibiotics since the 1940s, important aspects of how antibiotics clear bacterial 8 population at all levels (molecular, cellular and population) remain not well clarified.…”

mentioning

confidence: 99%

“…Even at concentrations above MIC, some bacteria survive a short-term exposure to 319 antibiotics before being affected by it. This ability of bacterial population is known as 320 tolerance [6]. In contrast to resistance, which is quantified by the MIC, tolerance is 321 poorly characterized.…”

mentioning

confidence: 99%

Theoretical investigation of stochastic clearance of bacteria: First-passage analysis

Teimouri

Kolomeisky

2018

Preprint

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Understanding mechanisms of bacterial eradication is critically important for overcoming failures of antibiotic treatments. Current studies suggest that the clearance of large bacterial populations proceeds deterministically, while for smaller populations the stochastic effects become more relevant. Here we develop a theoretical approach to investigate the bacterial population dynamics under the effect of antibiotic drugs using a method of first-passage processes. It allows us to explicitly evaluate the most important characteristics of the bacterial clearance dynamics such as extinction probabilities and extinction times. The new meaning of minimal inhibitory concentrations for stochastic clearance of bacterial populations is also discussed. In addition, we investigate the effect of fluctuations in the population growth rates on dynamics of bacterial eradication. It is found that extinction probabilities and extinction times generally do not correlate with each other when random fluctuations in the growth rates are taking place. Unexpectedly, for a significant range of parameters the extinction times increase due to these fluctuations, indicating a slowing in the bacterial clearance dynamics. It is argued that this might be one of the initial steps in the pathway for the development of antibiotic resistance. Furthermore, it is suggested that extinction times is a convenient measure of bacterial tolerance. 12 1/18 Majority of current experimental and theoretical studies focus on the eradication of 13 initially large quantities of bacteria [14,25,28], and it was shown that a deterministic 14 picture describes well the decrease in these bacterial populations [10,28]. In this 15 deterministic framework, the dynamics of bacterial population exposed to antibiotic is 16 characterized by a minimum inhibitory concentration (MIC), the minimal drug 17 concentration required to inhibit bacterial growth [10,13,28]. The MIC can be regarded 18 as a threshold on the antibiotic concentration such that only above MIC a bacterial 19 population can undergo full extinction, while for concentrations below MIC the infection 20 will never disappear. 21However, it can be argued that it is also critically important to investigate the 22 clearance dynamics for small bacterial populations. Failure to completely eradicate a 23 population of bacteria can have two main consequences. First, even a small number of 24 surviving bacteria can restore infections [19]. Second, certain strains of surviving cells 25 may develop antibiotic resistance, which, in turn, can complicate subsequent 26 therapies [11,17,20]. Therefore, the effective treatment of infections requires not only 27 reducing a large population number to a small number, but also the complete 28 eradication of the bacterial population [4,32,35]. 29Despite earlier technical problems [14,25], recent experiments were able to 30 quantitatively investigate the antibiotic-induced clearance of small bacterial 31 populations [9]. It was demonstrated that stochastic factors play much more impo...

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An Experimental Framework for Quantifying Bacterial Tolerance

Cited by 108 publications

References 30 publications

Genetic and Transcriptomic Analyses of Ciprofloxacin-TolerantStaphylococcus aureusIsolated by the Replica Plating Tolerance Isolation System (REPTIS)

Genetic and Transcriptomic Analyses of Ciprofloxacin-TolerantStaphylococcus aureusIsolated by the Replica Plating Tolerance Isolation System (REPTIS)

Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance in a model gut microbiota

Theoretical investigation of stochastic clearance of bacteria: First-passage analysis

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