Author Correction: Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community

Beardmore, Robert; Cook, Emily; Nilsson, Susanna; Smith, Adam R.; Tillmann, Anna; Esquivel, Brooke D.; Haynes, Ken; Gow, Neil A. R.; Brown, Alistair J. P.; White, Theodore C.; Gudelj, Ivana

doi:10.1038/s41559-018-0678-0

Cited by 3 publications

(8 citation statements)

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“…Subsequently, those parameters are used to numerically simulate the competition model (10) over multiple seasons, where each season is initiated at 0.1% glucose, predicting that C. albicans loses the competition (Fig 6(a), gray markers). In contrast, our approach is consistent with previous empirical studies (see Fig 1 (b) in [7]) predicting that C. albicans wins at 0.1% glucose concentration (Fig 6(a), brown markers).…”

Section: Albicans)supporting

confidence: 92%

“…Previous empirical studies observed that C. glabrata outcompetes C. albicans at high glucose concentrations [7]. Here we illustrate that estimating kinetic parameters at a high glucose concentration cannot accurately predict competition outcomes at low glucose concentrations as it gives an unfair growth advantage to C. glabrata over C. albicans (Fig 6(b)).…”

Section: Plos Computational Biologymentioning

confidence: 55%

“…Example 2: Predicting the outcomes of microbial competition. Next, we seek to predict the outcome of competition between two microbial species using the new approach proposed here and compare it to the predictions generated by a common approach that uses growth parameters estimated from a single initial nutrient concentration [7,9]. To this end, we consider a microbial community consisting of C. albicans and C. glabrata competing for limiting glucose in the environment.…”

Section: Predicting Microbial Growth and Competition Outcomes Across mentioning

confidence: 99%

“…Therefore, understanding and predicting microbial population dynamics is a key challenge for the fields of ecology, evolution, public health, and biotechnology. Mathematical models of microbial growth and metabolism form the foundation of many predictions regarding competition [7][8][9], metabolic interactions [10], cooperation [11][12][13][14][15], and diversification [16,17] within microbial communities, as well as product formation and its optimisation for use in biotechnology [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Predicting microbial growth dynamics in response to nutrient availability

et al. 2021

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Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker’s yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.

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Section: Albicans)supporting

confidence: 92%

Section: Plos Computational Biologymentioning

confidence: 55%

Section: Predicting Microbial Growth and Competition Outcomes Across mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting microbial growth dynamics in response to nutrient availability

et al. 2021

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“…Polymicrobial infections have been observed to have worse clinical outcomes in some cases ( 29 – 31 ), although these results are mixed ( 32 , 33 ). The metabolic interactions (both positive and negative) among these species have been demonstrated to impact antibiotic response ( 34 ). One such positive interaction is cross-feeding, in which one species produces an essential metabolite for another; this also occurs in infection contexts ( 35 ).…”

Section: Introductionmentioning

confidence: 99%

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community

Adamowicz

Harcombe

2020

Antimicrob Agents Chemother

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With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, wherein multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding co-culture. Using our previously described weakest link hypothesis, we hypothesized antibiotic interactions in co-culture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in co-culture. We tested the interactions between ten previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict co-culture patterns based solely on the weakest link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

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Weakest link dynamics predict apparent antibiotic interactions in a model cross-feeding community

Adamowicz

Harcombe

2020

Preprint

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13With the growing global threat of antimicrobial resistance, novel strategies are required for 14 combatting resistant pathogens. Combination therapy, wherein multiple drugs are used to treat an 15 infection, has proven highly successful in the treatment of cancer and HIV. However, this 16 practice has proven challenging for the treatment of bacterial infections due to difficulties in 17 selecting the correct combinations and dosages. An additional challenge in infection treatment is 18 the polymicrobial nature of many infections, which may respond to antibiotics differently than a 19 monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, 20 antagonism, or independence/additivity) in monoculture can be used to predict antibiotic 21 interactions in an obligate cross-feeding co-culture. Using our previously described weakest link 22 hypothesis, we hypothesized antibiotic interactions in co-culture based on the interactions we 23 observed in monoculture. We then compared our predictions to observed antibiotic interactions 24 in co-culture. We tested the interactions between ten previously identified antibiotic 25 combinations using checkerboard assays. Although our antibiotic combinations interacted 26 differently than predicted in our monocultures, our monoculture results were generally sufficient 27 to predict co-culture patterns based solely on the weakest link hypothesis. These results suggest 28 that combination therapy for cross-feeding multispecies infections may be successfully designed 29 based on antibiotic interaction patterns for their component species. 30 31 independent mutation is required for resistance to each drug (8, 9). This approach, of using 49 multiple drugs to target multiple essential targets, has also been used in cancer chemotherapy to 50 manage drug-resistant and genetically heterogeneous tumors (10, 11). In cases of bacterial 51 infections, multidrug therapy has been adopted in only a few specific infections, such as 52 treatment for drug sensitive tuberculosis (2). However, clinical trials of combination therapy in 53 the treatment of bacterial infections in patients have been limited. Choosing the correct drug 54 combination is difficult (12, 13), and efficacy has been mixed (14, 15). A greater understanding 55 4 of the mechanisms driving effective combination therapy are therefore required for successful 56 clinical implementation. 57The success of combination therapy is affected by interactions between drugs, wherein the 58 activity and effectiveness of one drug is impacted by the presence or absence of another (16). 59There are several mechanisms by which antibiotics may synergize (work more effectively or at 60 lower doses together than separately) or antagonize (work less effectively or at higher doses 61 together than separately). While the precise nature of these interactions depends on the drugs and 62 the bacterial species being targeted, some general mechanisms have been described for different 63 classes of antibiotics (17). Synergi...

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Author Correction: Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community

Cited by 3 publications

References 0 publications

Predicting microbial growth dynamics in response to nutrient availability

Predicting microbial growth dynamics in response to nutrient availability

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community

Weakest link dynamics predict apparent antibiotic interactions in a model cross-feeding community

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