Fast bacterial growth reduces antibiotic accumulation and efficacy
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Evaluation Summary:
This manuscript addresses mechanisms by which bacteria are able to survive and evade killing by antibiotics. Using fluorescent versions of antibiotics it studies whether if entry/efflux of the drug itself is a significant contributor to the observed variability of antibiotic activity. This study will be of interest to microbiologists and clinicians for design of better antibiotic therapies.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Phenotypic variations between individual microbial cells play a key role in the resistance of microbial pathogens to pharmacotherapies. Nevertheless, little is known about cell individuality in antibiotic accumulation. Here, we hypothesise that phenotypic diversification can be driven by fundamental cell-to-cell differences in drug transport rates. To test this hypothesis, we employed microfluidics-based single-cell microscopy, libraries of fluorescent antibiotic probes and mathematical modelling. This approach allowed us to rapidly identify phenotypic variants that avoid antibiotic accumulation within populations of Escherichia coli , Pseudomonas aeruginosa , Burkholderia cenocepacia, and Staphylococcus aureus . Crucially, we found that fast growing phenotypic variants avoid macrolide accumulation and survive treatment without genetic mutations. These findings are in contrast with the current consensus that cellular dormancy and slow metabolism underlie bacterial survival to antibiotics. Our results also show that fast growing variants display significantly higher expression of ribosomal promoters before drug treatment compared to slow growing variants. Drug-free active ribosomes facilitate essential cellular processes in these fast-growing variants, including efflux that can reduce macrolide accumulation. We used this new knowledge to eradicate variants that displayed low antibiotic accumulation through the chemical manipulation of their outer membrane inspiring new avenues to overcome current antibiotic treatment failures.
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Author Response
Reviewer #2 (Public Review):
The authors model drug uptake with a lag time (t0), after which there is a constant rate of drug uptake. But why is there such a lag time at all? This would suggest a positive feedback loop in drug binding. However, then one would not necessarily expect a constant drug uptake rate afterwards. The rationale for this model should be better explained. Correlating the fluorescence measurements (as in Fig. 1) with the single-cell elongation rates (as in Fig. 5) could help to identify if the lag in drug uptake coincides with the lag in cell growth.
We agree with the reviewer that the lag in drug uptake coincides with the lag in cell growth, our data in Figure 2 clearly support the correlation between lag in roxithromycin-NBD uptake and lag in cell growth during treatment. We also agree with the …
Author Response
Reviewer #2 (Public Review):
The authors model drug uptake with a lag time (t0), after which there is a constant rate of drug uptake. But why is there such a lag time at all? This would suggest a positive feedback loop in drug binding. However, then one would not necessarily expect a constant drug uptake rate afterwards. The rationale for this model should be better explained. Correlating the fluorescence measurements (as in Fig. 1) with the single-cell elongation rates (as in Fig. 5) could help to identify if the lag in drug uptake coincides with the lag in cell growth.
We agree with the reviewer that the lag in drug uptake coincides with the lag in cell growth, our data in Figure 2 clearly support the correlation between lag in roxithromycin-NBD uptake and lag in cell growth during treatment. We also agree with the reviewer that other intracellular mechanisms could contribute to lag in drug uptake including a positive feedback loop in drug binding or a positive feedback loop between efflux and drug accumulation (Le et al. mBio 12, e00676, 2021).
We have added this new information on lines 330-333 of our revised manuscript.
We have also rephrased the text on lines 103-104 and 216-217 to clarify that the drug uptake rate is not constant and that we have taken this into account by modelling the drug uptake rate as a dynamical variable (described by the second differential equation of the model).
Finally, we have now explicitly stated that the primary focus of our model is to capture the phenomenology of drug accumulation in our experiments, for example in terms of the measured lag in drug uptake and the time-varying uptake rate. For this reason we have not made assumptions regarding the underlying biological mechanisms (e.g. positive feedback). However, more detailed models will be necessary in the future as we begin to dissect the mechanisms underlying antibiotic accumulation dynamics in individual cells.
We have now discussed this information on lines 209-212 of our revised manuscript.
The fact that t0 and k1 are always strongly anti-correlated suggests that these two fit parameters are not independent and simply reflect the same underlying process. It would be critical to clarify this point for the entire analysis of correlations between fit parameters. To this end, the confidence intervals for the fit parameters and their correlations should be estimated using a suitable numerical optimization algorithm. It only makes sense to interpret correlations between fit parameters obtained from different cells if such an analysis shows that the fit parameters are independent (uncorrelated or only weakly correlated) for each individual cell.
Following the reviewer's input we have measured the correlation between the different kinetic parameters for each individual cell. We found that for 86% and 79% of cells across all antibiotic treatments there was a positive correlation between t0 and k1 and between t0 and Fmax in the posterior Bayesian distribution (i.e. the distribution of parameter values for which the model behaviour matches the data). In contrast, at the population level (using the maximum likelihood estimate for each cell) we found a significantly negative correlation between t0 and k1 for the accumulation of polymyxin B, octapeptin and roxithromycin probes and between t0 and Fmax for the accumulation of polymyxin B, octapeptin, linezolid and trimethoprim probes (Table 2). Finally, we found a significantly positive correlation between k1 and Fmax for the accumulation of polymyxin B, ciprofloxacin and roxithromycin probes (Table 2). The latter correlation was partially imposed by the definition of Fmax in the model (as already acknowledged in the submitted manuscript) and in fact we found that 78% of the cells displayed a positive correlation between these two parameters at the single-cell level. Taken together these data demonstrate that the measured negative correlation between t0 and k1 for the accumulation of polymyxin B, octapeptin and roxithromycin is not due to the fact that t0 and k1 reflect the same underlying process.
This new information is discussed on lines 267-270 and 275-277 of our revised manuscript.
The results in Figure 4 are confusing: It seems unlikely that cells are a large enough 'antibiotic sink' to protect neighbouring cells, especially given that cells do not seem to be affected by the nutrient uptake of their neighbours. Furthermore, the opposite correlation (where drug accumulation increases with more 'screening' cells) is very hard to rationalize. A plausible explanation for this effect would be needed. Here, it would be helpful to estimate the molecule numbers (concentrations), uptake rates, and diffusion coefficients of the different antibiotics, compare them to those of nutrient molecules in the growth medium, and explain based on this why the 'screening' cells can have different effects for these molecules on the relevant time and length scales. Without more support there is a concern that these observations may be due to technical artefacts.
Following the reviewer input we have now explicitly advanced the hypothesis that delayed accumulation of membrane targeting drugs in bacteria screened by other cells could be explained by a transient reduction in the extracellular drug concentration around these bacteria (compared to the concentration in the main microfluidic chamber) due to rapid drug binding to the membranes of screening cells. In accordance with this hypothesis, when we run 2-dimensional numerical simulations of drug diffusion in channels hosting bacteria with a high drug absorption rate (g=0.2 mol m-2 s-1, see Methods), we found a gradient in extracellular drug concentration along the channel length: for the first 90min post drug addition, the concentration was highest around the bacterium without screens and lowest around the bacterium with four screens (new Figure 3-figure supplement 1A). On the contrary, in the presence of bacteria with a low absorption rate (g=0.002 mol m-2 s-1), the extracellular drug concentration equilibrated along the channel length within 2min post drug addition to the device (Figure 3-figure supplement 1C). Accordingly, in the presence of bacteria with high absorption rate, the intracellular drug concentration (that we simply modelled as concentration at the bacterial surface) reached saturation levels in the bacterium without screens within minutes post drug addition, whereas the bacterium with 4 screens reached saturation levels 90min post drug addition (Figure 3-figure supplement 1B). Conversely, bacteria with low absorption rate slowly accumulated the drug independently on the number of screens (Figure 3-figure supplement 1D). Therefore, according to these simplified 2-dimensional transport simulations (i.e. we do not take into account neither efflux nor transport across the gram-negative double barrier), delayed accumulation of membrane targeting drugs in bacteria screened by other cells is due to a transient reduction in the extracellular drug concentration around these bacteria, whereas other mechanisms must underpin increased roxithromycin accumulation in screened bacteria and this phenomenon should be investigated further in future studies. Finally, we would also like to reiterate that mechanisms other than the microcolony architecture must underlie phenotypic variants with reduced antibiotic accumulation. In fact, we registered significant cell-cell differences in antibiotic accumulation even within the same subpopulation of bacteria with the same number of screening cells (Fig. 3).
These new data are discussed in the revised manuscript on lines 386-406 and 796-815 (where we have now reported details about the COMSOL simulations) and in the new Figure 3-figure supplement 1.
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Evaluation Summary:
This manuscript addresses mechanisms by which bacteria are able to survive and evade killing by antibiotics. Using fluorescent versions of antibiotics it studies whether if entry/efflux of the drug itself is a significant contributor to the observed variability of antibiotic activity. This study will be of interest to microbiologists and clinicians for design of better antibiotic therapies.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
Lapinska et al., address the contribution of heterogeneity in antibiotic uptake towards bacterial survival against antibiotic treatment. To achieve specific inhibitory activity, the antibiotic compound needs to bind its target which could be on the outer cell wall, embedded in the membrane or intracellular within the cell. Therefore, the effective concentration of the compound able to reach the target is influenced by several variables such as size of the compound, permeability of the membrane, influx and efflux transport processes, stability of the compound etc. As each of these processes are heterogenous amongst individual members of an isogenic population, this generates extreme variability in uptake of compounds by individual cells.
Lapinska et al., utilize previously reported fluorescently-tagged …Reviewer #1 (Public Review):
Lapinska et al., address the contribution of heterogeneity in antibiotic uptake towards bacterial survival against antibiotic treatment. To achieve specific inhibitory activity, the antibiotic compound needs to bind its target which could be on the outer cell wall, embedded in the membrane or intracellular within the cell. Therefore, the effective concentration of the compound able to reach the target is influenced by several variables such as size of the compound, permeability of the membrane, influx and efflux transport processes, stability of the compound etc. As each of these processes are heterogenous amongst individual members of an isogenic population, this generates extreme variability in uptake of compounds by individual cells.
Lapinska et al., utilize previously reported fluorescently-tagged antibiotics, targeting different subcellular locations, to measure accumulation dynamics at the single-cell level. As expected they observe heterogeneity in antibiotic accumulation both in gram-negative and gram-positive bacteria. Antibiotics targeting the outer membrane were observed to label their targets faster and earlier compared to antibiotics targeting intracellular components. They implement a mathematical model based on two differential equations which they use to infer kinetic parameters for the different compounds. These variability in antibiotic accumulation had functional consequence in that cells with no uptake were the ones that continued to grow in presence of the antibiotic and survived. Interestingly, at least for roxithromycin treatment, cells that were growing faster were observed to survive better which goes against the conventional belief that fast growing cells are more susceptible to antibiotics. The heterogeneity in compound accumulation was shown to be a function of ribosomal activity and levels of an efflux pump protein. Finally, deletion of the gene encoding the efflux pump or treatment with compounds to increase permeability caused faster accumulation of the antibiotic even though the heterogeneity was still maintained.The authors have done a commendable job by choosing different classes of antibiotics and analysing quantitatively the drug accumulation in individual cells. However, they often tend to generalize their conclusions. From their observations, the take home message seems to be that each compound behaves uniquely in terms of accumulation and consequences. Still the use of fluorescent antibiotics to address this often-overlooked aspect in heterogenous response to antibiotics is definitely appreciated.
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Reviewer #2 (Public Review):
The authors claim that phenotypic heterogeneity underlies heterogeneity in drug uptake, which in turn underlies heterogeneity in antibiotic-induced growth suppression. They characterize the uptake kinetics for a range of different antibiotics at the single cell level and find that membrane-targeting drugs accumulate significantly faster than other drugs. In contrast to the common belief that fast growth promotes drug sensitivity, they find the opposite trend for roxithromycin. They claim that growth-rate dependent drug efflux pump expression, but not drug influx porins, are largely responsible for this.
Quantifying the kinetics of drug uptake at the single cell level and correlating it with phenotypes is an important problem of great interest to a broad audience. The single cell approach to characterize drug …
Reviewer #2 (Public Review):
The authors claim that phenotypic heterogeneity underlies heterogeneity in drug uptake, which in turn underlies heterogeneity in antibiotic-induced growth suppression. They characterize the uptake kinetics for a range of different antibiotics at the single cell level and find that membrane-targeting drugs accumulate significantly faster than other drugs. In contrast to the common belief that fast growth promotes drug sensitivity, they find the opposite trend for roxithromycin. They claim that growth-rate dependent drug efflux pump expression, but not drug influx porins, are largely responsible for this.
Quantifying the kinetics of drug uptake at the single cell level and correlating it with phenotypes is an important problem of great interest to a broad audience. The single cell approach to characterize drug uptake kinetics presented in this work has considerable potential and can provide new insights into cell-cell variability in the context of antibiotic sensitivity. The conclusion that roxithromycin sensitivity decreases with cellular growth is interesting but the interpretation and contextualization of this result should be improved. In principle, investigating which molecular players contribute to drug uptake dynamics could significantly strengthen the manuscript; this aspect will require more analysis and experiments. Moreover, some relevant control experiments seem to be missing and the enormous heterogeneity in single cell growth rate may indicate issues with the assay design.
Major suggestions
* The authors model drug uptake with a lag time (t0), after which there is a constant rate of drug uptake. But why is there such a lag time at all? This would suggest a positive feedback loop in drug binding. However, then one would not necessarily expect a constant drug uptake rate afterwards. The rationale for this model should be better explained. Correlating the fluorescence measurements (as in Fig. 1) with the single-cell elongation rates (as in Fig. 5) could help to identify if the lag in drug uptake coincides with the lag in cell growth.
* The fact that t0 and k1 are always strongly anti-correlated suggests that these two fit parameters are not independent and simply reflect the same underlying process. It would be critical to clarify this point for the entire analysis of correlations between fit parameters. To this end, the confidence intervals for the fit parameters and their correlations should be estimated using a suitable numerical optimization algorithm. It only makes sense to interpret correlations between fit parameters obtained from different cells if such an analysis shows that the fit parameters are independent (uncorrelated or only weakly correlated) for each individual cell.
* The results in Figure 4 are confusing: It seems unlikely that cells are a large enough 'antibiotic sink' to protect neighbouring cells, especially given that cells do not seem to be affected by the nutrient uptake of their neighbours. Furthermore, the opposite correlation (where drug accumulation increases with more 'screening' cells) is very hard to rationalize. A plausible explanation for this effect would be needed. Here, it would be helpful to estimate the molecule numbers (concentrations), uptake rates, and diffusion coefficients of the different antibiotics, compare them to those of nutrient molecules in the growth medium, and explain based on this why the 'screening' cells can have different effects for these molecules on the relevant time and length scales. Without more support there is a concern that these observations may be due to technical artefacts.
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