The inoculum effect and band‐pass bacterial response to periodic antibiotic treatment

Tan, Cheemeng; Smith, Robert P.; Srimani, Jaydeep K.; Riccione, Katherine A.; Prasada, Sameer; Kuehn, Meta J.; Li, You

doi:10.1038/msb.2012.49

Cited by 87 publications

(105 citation statements)

References 55 publications

(69 reference statements)

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“…These considerations are not applicable to the systems we study here, since wild type cells grew homogeneously in the presence of antibiotics tested, and only cells expressing drug resistance exhibited growth bistability when cultured in the presence of antibiotics. The observed growth bistability is also unlikely to arise from to a recently described inoculum effect (55), in which two separate cultures with identical concentration of certain drugs may exhibit distinct growth rates depending on the culture inoculant density: First, bacteriostatic drugs investigated here (Cm and Tc) have been shown not to exhibit the inoculum effect (55, 56). Second, the inoculum effect concerns the differences between separate cultures, whereas we observed coexistence of growing and non-growing subpopulations in a single homogeneous culture.…”

Section: Discussionmentioning

confidence: 70%

The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria

Deris

Kim

Zhang

et al. 2013

Science

181

221

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To predict the emergence of antibiotic resistance, quantitative relations must be established between the fitness of drug resistant organisms and the molecular mechanisms conferring resistance. These relations are often unknown and may depend on the state of bacterial growth. To bridge this gap, we have investigated Escherichia coli strains expressing resistance to translation-inhibiting antibiotics. We show that resistance expression and drug inhibition are linked in a positive feedback loop arising from an innate, global effect of drug-inhibited growth on gene expression. A quantitative model of bacterial growth based on this innate feedback accurately predicts the rich phenomena observed: a plateau-shaped fitness landscape, with an abrupt drop in the growth rates of cultures at a threshold drug concentration, and the coexistence of growing and non-growing populations, i.e., growth bistability, below the threshold.

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Section: Discussionmentioning

confidence: 70%

The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria

Deris

Kim

Zhang

et al. 2013

Science

181

221

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“…While the qualitative concept of cellular phenotype as a product of protein(s) function is clear, the ability to deconstruct and accurately map the phenotype to physicochemical properties of a specific protein within the larger cellular milieu is an interesting challenge (Tan et al , 2012; Walkiewicz et al , 2012). We describe a general mathematical model that allows us to relate changes in fitness function to changes in biophysical behaviors and could be applied to any effluxer that contributes strongly to fitness over a set of environmental conditions.…”

Section: Resultsmentioning

confidence: 99%

Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer

Perez

Gomez

Kalvapalle

et al. 2017

Molecular Systems Biology

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The major facilitator superfamily (MFS) effluxers are prominent mediators of antimicrobial resistance. The biochemical characterization of MFS proteins is hindered by their complex membrane environment that makes in vitro biochemical analysis challenging. Since the physicochemical properties of proteins drive the fitness of an organism, we posed the question of whether we could reverse that relationship and derive meaningful biochemical parameters for a single protein simply from fitness changes it confers under varying strengths of selection. Here, we present a physiological model that uses cellular fitness as a proxy to predict the biochemical properties of the MFS tetracycline efflux pump, TetB, and a family of single amino acid variants. We determined two lumped biochemical parameters roughly describing K m and V max for TetB and variants. Including in vivo protein levels into our model allowed for more specified prediction of pump parameters relating to substrate binding affinity and pumping efficiency for TetB and variants. We further demonstrated the general utility of our model by solely using fitness to assay a library of tet(B) variants and estimate their biochemical properties.

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“…Unfortunately, the development of novel drugs is a long and arduous process, underscoring the need for alternative approaches to forestall resistance evolution. Recent work has highlighted the promise of evolution-based strategies for optimizing and prolonging the efficacy of established drugs, including optimal dose scheduling 7-9 , antimicrobial stewardship 10,11 , drug cycling [12][13][14] , consideration of spatial dynamics 15,16 , cooperative dynamics [17][18][19][20] , or phenotypic resistance [21][22][23] , and judicious use of drug combinations [24][25][26][27][28][29][30][31] . In a similar spirit, a number of recent studies have suggested exploiting collateral sensitivity as a means for slowing or even reversing antibiotic resistance [32][33][34][35] .…”

Section: Introductionmentioning

confidence: 99%

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

Maltas

Wood

2017

Preprint

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The growing threat of drug resistance has inspired a surge in evolution-based strategies for optimizing the efficacy of antibiotics. One promising approach involves harnessing collateral sensitivity-the increased susceptibility to one drug accompanying resistance to a different drug-to mitigate the spread of resistance. Unfortunately, because the mechanisms of collateral sensitivity are diverse and often poorly understood, the systematic design of multi-drug treatments based on these evolutionary trade-offs is extraordinarily difficult. In this work, we provide an extensive phenotypic characterization of collateral drug effects in E. faecalis, a gram-positive species among the leading causes of nosocomial infections. By combining parallel experimental evolution with high-throughput dose-response measurements, we provide quantitative profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict, as even mutants selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity. Overall, variability in collateral profiles is strongly correlated with the final level of resistance to the selecting drug. In addition, collateral effects to certain drugs (e.g. ceftriaxone) are considerably more variable than those to other drugs (e.g. fosfomycin), even for drugs from the same class. Remarkably, however, the sensitivity profiles cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the underlying statistical structure in the collateral profiles, we develop a simple mathematical framework based on a Markov decision process (MDP) to identify optimal antibiotic cycling policies that maximize expected collateral sensitivity. Importantly, these cycles can be tuned to optimize long-term treatment outcomes, leading to drug sequences that may produce long-term collateral sensitivity at the expense of short-term collateral resistance.

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The inoculum effect and band‐pass bacterial response to periodic antibiotic treatment

Cited by 87 publications

References 55 publications

The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria

The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria

Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

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