Limits...
Using a sequential regimen to eliminate bacteria at sublethal antibiotic dosages.

Fuentes-Hernandez A, Plucain J, Gori F, Pena-Miller R, Reding C, Jansen G, Schulenburg H, Gudelj I, Beardmore R - PLoS Biol. (2015)

Bottom Line: Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics.Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse.These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

View Article: PubMed Central - PubMed

Affiliation: Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México.

ABSTRACT
We need to find ways of enhancing the potency of existing antibiotics, and, with this in mind, we begin with an unusual question: how low can antibiotic dosages be and yet bacterial clearance still be observed? Seeking to optimise the simultaneous use of two antibiotics, we use the minimal dose at which clearance is observed in an in vitro experimental model of antibiotic treatment as a criterion to distinguish the best and worst treatments of a bacterium, Escherichia coli. Our aim is to compare a combination treatment consisting of two synergistic antibiotics to so-called sequential treatments in which the choice of antibiotic to administer can change with each round of treatment. Using mathematical predictions validated by the E. coli treatment model, we show that clearance of the bacterium can be achieved using sequential treatments at antibiotic dosages so low that the equivalent two-drug combination treatments are ineffective. Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics. Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse. However, dual resistance due to the pump means that the antibiotics must be carefully deployed and not all sublethal sequential treatments succeed. A screen of 136 96-h-long sequential treatments determined five of these that could clear the bacterium at sublethal dosages in all replicate populations, even though none had done so by 24 h. These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

No MeSH data available.


Related in: MedlinePlus

A collateral sensitivity at IC70 with respect to per capita growth rate, R.(A) Optical density time series data used in Fig. 4 was reused by fitting a logistic growth model (defined in the text) to estimate growth rates. For clarity, both the growth rate parameter R and the regression coefficient R2 from exemplar fits are indicated alongside modelled dynamics. (B) The resulting dataset shows significant and nonsignificant collateral sensitivities with respect to growth rate (R) following an exchange of antibiotic (t tests, n = 5). (S1 Data contains the data used in this figure.)
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4390231&req=5

pbio.1002104.g006: A collateral sensitivity at IC70 with respect to per capita growth rate, R.(A) Optical density time series data used in Fig. 4 was reused by fitting a logistic growth model (defined in the text) to estimate growth rates. For clarity, both the growth rate parameter R and the regression coefficient R2 from exemplar fits are indicated alongside modelled dynamics. (B) The resulting dataset shows significant and nonsignificant collateral sensitivities with respect to growth rate (R) following an exchange of antibiotic (t tests, n = 5). (S1 Data contains the data used in this figure.)

Mentions: A second cross sensitivity property of the ERY—DOX was also established, as follows. Having found a mechanism for an NCS with respect to population densities, we hypothesised that the (n+1)-protocol data could exhibit cross sensitivities with respect to other measures of bacterial fitness. To demonstrate this, we fitted the logistic growth model to bacterial density time series, where the parameter R is per hour per capita growth rate and K is the population carrying capacity. The resulting data exhibits collateral sensitivities irrespective of the order in which the drugs were exchanged because a reduction of R was observed following a change of drug for every n tested (from 3 to 6), although not all reductions were significant (Fig. 6).


Using a sequential regimen to eliminate bacteria at sublethal antibiotic dosages.

Fuentes-Hernandez A, Plucain J, Gori F, Pena-Miller R, Reding C, Jansen G, Schulenburg H, Gudelj I, Beardmore R - PLoS Biol. (2015)

A collateral sensitivity at IC70 with respect to per capita growth rate, R.(A) Optical density time series data used in Fig. 4 was reused by fitting a logistic growth model (defined in the text) to estimate growth rates. For clarity, both the growth rate parameter R and the regression coefficient R2 from exemplar fits are indicated alongside modelled dynamics. (B) The resulting dataset shows significant and nonsignificant collateral sensitivities with respect to growth rate (R) following an exchange of antibiotic (t tests, n = 5). (S1 Data contains the data used in this figure.)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390231&req=5

pbio.1002104.g006: A collateral sensitivity at IC70 with respect to per capita growth rate, R.(A) Optical density time series data used in Fig. 4 was reused by fitting a logistic growth model (defined in the text) to estimate growth rates. For clarity, both the growth rate parameter R and the regression coefficient R2 from exemplar fits are indicated alongside modelled dynamics. (B) The resulting dataset shows significant and nonsignificant collateral sensitivities with respect to growth rate (R) following an exchange of antibiotic (t tests, n = 5). (S1 Data contains the data used in this figure.)
Mentions: A second cross sensitivity property of the ERY—DOX was also established, as follows. Having found a mechanism for an NCS with respect to population densities, we hypothesised that the (n+1)-protocol data could exhibit cross sensitivities with respect to other measures of bacterial fitness. To demonstrate this, we fitted the logistic growth model to bacterial density time series, where the parameter R is per hour per capita growth rate and K is the population carrying capacity. The resulting data exhibits collateral sensitivities irrespective of the order in which the drugs were exchanged because a reduction of R was observed following a change of drug for every n tested (from 3 to 6), although not all reductions were significant (Fig. 6).

Bottom Line: Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics.Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse.These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

View Article: PubMed Central - PubMed

Affiliation: Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México.

ABSTRACT
We need to find ways of enhancing the potency of existing antibiotics, and, with this in mind, we begin with an unusual question: how low can antibiotic dosages be and yet bacterial clearance still be observed? Seeking to optimise the simultaneous use of two antibiotics, we use the minimal dose at which clearance is observed in an in vitro experimental model of antibiotic treatment as a criterion to distinguish the best and worst treatments of a bacterium, Escherichia coli. Our aim is to compare a combination treatment consisting of two synergistic antibiotics to so-called sequential treatments in which the choice of antibiotic to administer can change with each round of treatment. Using mathematical predictions validated by the E. coli treatment model, we show that clearance of the bacterium can be achieved using sequential treatments at antibiotic dosages so low that the equivalent two-drug combination treatments are ineffective. Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics. Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse. However, dual resistance due to the pump means that the antibiotics must be carefully deployed and not all sublethal sequential treatments succeed. A screen of 136 96-h-long sequential treatments determined five of these that could clear the bacterium at sublethal dosages in all replicate populations, even though none had done so by 24 h. These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

No MeSH data available.


Related in: MedlinePlus