Meriem El Karoui

Antibiotic combinations aim to maximise drug treatment efficiency and minimise resistance evolution, but a full understanding of their effect on bacterial cells is lacking. The interaction between the DNA-damaging antibiotic ciprofloxacin and the translation inhibitor tetracycline is antagonistic, resulting in a weaker effect on bacterial growth than expected from each drug individually. While this antagonism has been analysed at the population level, it has not been investigated at the single-cell level. We used a microfluidic device to quantify the antagonism between ciprofloxacin and tetracycline in single bacterial cells under three nutrient conditions. Improved growth results from increased survival of cells under the drug combination compared to ciprofloxacin alone. This effect depends on the initial drug-free growth rate, with better suppression in nutrient-rich conditions. Quantifying the DNA damage response (SOS response) revealed two sub-populations among cells that died upon ciprofloxacin treatment. The larger low-SOS sub-population, which showed increased survival compared to high-SOS cells, explains the stronger antagonistic effect in nutrient-rich conditions. Our results underscore the importance of single-cell quantification in understanding bacterial responses to antibiotic combinations.

Single-cell technologies have led to many insights on the function of individual cells within populations. Time-series data, in particular, have become increasingly adopted to uncover the molecular basis of heterogeneity in processes such as gene regulation, growth adaptation, and drug resistance. Yet the analysis of single-cell trajectories often requires manual and application-dependent techniques to extract meaningful features that correlate with the biological processes of interest. Here, we employ representation learning to automatically encode time-series into a low-dimensional feature space that can be used for downstream prediction tasks. We trained deep variational autoencoders on cell length data from Escherichia coli cells growing in a “mother machine” device that allows tracking single cells over long periods. We show that the learned representations preserve the structure of the data across various growth media. Using the pretrained model in tandem with supervised learning, we encoded fresh time-series from cells exposed to single and combination antibiotics, which achieved excellent classification accuracy across drug treatments. Moreover, we demonstrate that the learned embeddings can be used to accurately classify datasets from different laboratories and growth conditions without retraining. This demonstrates that the autoencoder extracts meaningful information with promising generalization power. Our results highlight the potential of representation learning for the analysis of single-cell responses under chemical perturbations and growth conditions.

All living organisms have developed strategies to respond to chromosomal damage and preserve genome integrity. One such response is the repair of DNA double-strand breaks (DSBs), one of the most toxic forms of DNA lesions. In Escherichia coli, DSBs are repaired via RecBCD-dependent homologous recombination. RecBCD is essential for accurate chromosome maintenance, but its over-expression can lead to reduced DNA repair ability. This apparent paradox suggests that RecBCD copy numbers may need to be tightly controlled within an optimal range. Using single-molecule fluorescence microscopy, we have established that RecB is present in very low abundance at mRNA and protein levels. RecB transcription shows high fluctuations, yet cell-to-cell protein variability remains remarkably low. Here, we show that the post-transcriptional regulator Hfq binds to recB mRNA and downregulates RecB protein translation in vivo. Furthermore, specific disruption of the Hfq-binding site leads to more efficient translation of recB mRNAs. In addition, we observe a less effective reduction of RecB protein fluctuations in the absence of Hfq. This fine-tuning Hfq-mediated mechanism might have the underlying physiological function of maintaining RecB protein levels within an optimal range.

While significant efforts have been made to model bacterial cell division, few models have incorporated DNA replication into the control of this process. To date, models that attempt to capture the coordination between replication and division cycles are based on fundamentally different assumptions, and yet conflicting results have emerged. As a result, key questions regarding how replication affects cell size at division remain unclear. To address this, we develop in a first part, a robust mathematical framework to study models of coordination of replication and division cycles proposed in the literature. Through theoretical analysis, we highlight necessary and sufficient conditions to apply to replication-agnostic and division-agnostic models to ensure physiologically-coherent behaviors. Then, in a second part, we lead a comprehensive statistical analysis to assess the ability of the models to reproduce the division volume distribution conditioned on cell covariates. This in-depth analysis highlighted remarkable performances of a novel model, yielding promising results for future refinements toward a universal model of replication-division coordination in E. coli .

Efficient DNA repair is essential for maintaining genome integrity and ensuring cell survival. In Escherichia coli, RecBCD plays a crucial role in processing DNA ends, following a DNA double-strand break (DSB), to initiate repair. While RecBCD has been extensively studied in vitro, less is known about how it contributes to rapid and efficient repair in living bacteria. Here, we use single-molecule microscopy to investigate DNA repair in real time in E. coli. We quantify RecB single-molecule mobility and monitor the induction of the DNA damage response (SOS response) in individual cells. We show that RecB binding to DNA ends caused by endogenous processes leads to efficient repair without SOS induction. In contrast, repair is less efficient in the presence of exogenous damage or in a mutant strain with modified RecB activities, leading to high SOS induction. Our findings reveal how subtle alterations in RecB activity profoundly impact the efficiency of DNA repair in E. coli.

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All living organisms have developed strategies to respond to chromosomal damage and preserve genome integrity. One such response is the repair of DNA double-strand breaks (DSBs), one of the most toxic forms of DNA lesions. In Escherichia coli, DSBs are repaired via RecBCD-dependent homologous recombination. RecBCD is essential for accurate chromosome maintenance, but its over-expression can lead to reduced DNA repair ability. This apparent paradox suggests that RecBCD copy numbers may need to be tightly controlled within an optimal range. Using single-molecule fluorescence microscopy, we have established that RecB is present in very low abundance at mRNA and protein levels. RecB transcription shows high fluctuations, yet cell-to-cell protein variability remains remarkably low. Here, we show that the post-transcriptional regulator Hfq binds to recB mRNA and down-regulates RecB protein translation in vivo. Furthermore, specific disruption of the Hfq-binding site leads to more efficient translation of recB mRNAs. In addition, we observe a less effective reduction of RecB protein fluctuations in the absence of Hfq. This fine-tuning Hfq-mediated mechanism might have the underlying physiological function of maintaining RecB protein levels within an optimal range.

Repairing DNA damage is of primary importance for all living organisms. DNA double-strand breaks (DSBs) are one of the most serious types of DNA damage, as they lead to loss of genetic information and death when not repaired. In E. coli , they are recognised and processed by the RecBCD complex, which initiates repair by homologous recombination. Although the repair dynamics down-stream of RecBCD has been well characterised, it is still unclear how long this complex stays attached to DNA and what triggers its dissociation in vivo . To answer these questions, we imaged RecB at the single-molecule level, and quantified its dynamic behaviour in bacterial cells exposed to ciprofloxacin, an antibiotic that induces DSBs. Our results show that RecB forms long-lived complexes with DSBs (10 seconds), and that its dissociation from DNA is an intrinsic property of the complex, that does not depend on the amount of DNA damage, nor the following steps in the repair pathway. Moreover, we show that we can use RecB binding to DSBs as a marker to estimate the rate of damage formation. This study provides a detailed quantitative insight into the interaction of RecBCD with DNA double-strand ends in E. coli in vivo , and into the bacterial response to DSBs induced by ciprofloxacin.

In Escherichia coli the response to DNA damage shows strong cell-to-cell-heterogenity. This results in a random delay in cell division and asymmetrical binary fission of single cells, which can compromise the size homeostasis of the population. To quantify the effect of the heterogeneous response to genotoxic stress (called SOS response in E. coli) on the growth of the bacterial population, we propose a flexible time-continuous parametric model of individual-based population dynamics. We construct a stochastic model based on the "adder" size-control mechanism, extended to incorporate the dynamics of the SOS response and its effect on cell division. The model is fitted to individual lineage data obtained in a 'mother machine' microfluidic device. We show that the heterogeneity of the SOS response can bias the observed division rate. In particular, we show that the adder division rate is decreased by SOS induction and that this perturbative effect is stronger in fast-growing conditions.