Biology subjects

Chait, R.

Publications and source records attributed to Chait, R..

5 recordsLinked to original sources

bioRxiv2026

Biocontainment of phages inhibits bacterial clearance in micro niches

Phage therapy offers a promising solution to the antimicrobial resistance crisis. However, a major concern preventing the adoption of phage therapy is the potential for unintended consequences of phage release; both in regard to preventing the spread of phage resistance, and the proliferation of a non-endemic virus into the microbial ecosystem. Conditional replication (biocontainment) of phages through bioengineering may address these concerns, but the impact on bactericidal efficacy is unknown. Here, we created a biocontained T7 phage (T7{Delta}capsid) lacking the major structural capsid gene, gp10AB, that can only replicate on Escherichia coli strains expressing gp10AB in trans, and assessed its bactericidal efficacy compared with wild-type T7. Congruent with model predictions, T7{Delta}capsid was only able to clear a well-mixed culture of E. coli at a multiplicity of infection (MOI) of 10 or higher, whereas wild-type T7 prohibited growth at an MOI of 0.1. The reduction in efficacy was more evident in a complex structured environment within a microfluidic device, where phage success depends on its ability to penetrate a microbial niche via propagation. In this environment, T7{Delta}capsid was unable to propagate into the bacterial population and unlike wild-type T7, had no impact on the populations growth. This study shows that whilst biocontainment of phages may improve the biosafety of phage therapy, it comes at the cost of its propagation efficacy and niche penetration in relevant environments.

microbiology↗

bioRxiv2025

Stable coexistence and transport of lytic phage infections with migrating bacterial hosts

Bacteriophages (phages), viruses that exclusively infect bacteria, coexist with their bacterial hosts across diverse environments at densities exceeding 107 ml-1 in marine surface waters, 108 ml-1 in soils, and 109 ml-1 in the human gut1-6. In contrast, phage lysis of bacteria populations within well-mixed in vitro environments select for the emergence of phage-resistant bacterial mutants7, which in turn select for host-range expansion phage mutants8,9 and, typically, the collapse of phage populations altogether10,11. This gap in outcome raises a question: what enables long-term phage-bacteria coexistence? Here, we show how interactions in space can facilitate stable coexistence and long-range transport of virulent phage along with migrating bacteria. Combining theory, simulation, and experiments across multiple phage-bacteria systems, we reveal a chemotaxis-driven mechanism that robustly stabilizes coexistence and dispersal of virulent phages with migrating hosts. These findings suggest the ecological relevance of spatial interaction mechanisms that reinforce stability between antagonistic partners in the absence of perpetual cycles of defense and counter-defense, which may be broadly applicable across phage-bacteria systems.

ecology↗

bioRxiv2024

Antibiotic resistant bacteria survive treatment by doubling while shrinking

Many antibiotics that are used in healthcare, farming and aquaculture end up in environments with different spatial structures that might promote heterogeneity in the emergence of antibiotic resistance. However, experimental evolution of microbes at sub-inhibitory concentrations of antibiotics has been mainly carried out at the population level which does not allow capturing heterogeneity within bacterial populations. Here we investigate and compare the emergence of resistance to ciprofloxacin in Escherichia coli in well mixed and structured environments using experimental evolution, genomics and microfluidics-based time-lapse microscopy. We discover that resistance to ciprofloxacin and cross-resistance to other antibiotics is stronger in the well-mixed environment due to the emergence of target mutations, whereas efflux regulator mutations emerge in the structured environment. The latter mutants also harbour sub-populations of persisters that survive high concentrations of ciprofloxacin that inhibit bacterial growth at the population level. In contrast, genetically resistant bacteria that display target mutations also survive high concentrations of ciprofloxacin that inhibit their growth via population-level antibiotic tolerance. These resistant and tolerant bacteria keep doubling while shrinking in size in the presence of ciprofloxacin and regain their original size after antibiotic removal, which constitutes a newly discovered phenotypic response. This new knowledge sheds light on the diversity of strategies employed by bacteria to survive antibiotics and poses a stepping stone for understanding the link between mutations at the population level and phenotypic single-cell responses.

microbiology↗

bioRxiv2023

Investigating bacteria-phage interaction dynamics using droplet-based technology

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative approaches for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 (Escherichia virus T7) and two phage species with RNA genomes, MS2 (Emesvirus zinderi) and Q{beta} (Qubevirus durum). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25 hours, allowing us to determine growth rates of bacterial populations helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic and single-cell techniques will expedite research towards understanding the genetic and molecular basis of rapid phage-induced lysis, preempting bacterial resistance to phages and ultimately identifying key factors influencing the success of phage therapy.

microbiology↗

bioRxiv2021

Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level

Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system. Author summaryA common result for the modelling of cellular processes is that available data is not sufficiently rich to uniquely determine the biological mechanism or even just to ensure identifiability of parameters of a given model. Perturbing cellular processes with informative input stimuli and measuring dynamical responses may alleviate this problem. With the development of novel experimental platforms, we are now in a position to parallelise such perturbation experiments at the single cell level. This raises a plethora of new questions. Is it more informative to diversify input perturbations but to observe only few cells for each input or should we rather ensure that many cells are observed for only few inputs? How can we calculate likelihoods and infer parameters of stochastic kinetic models from data sets in which each cell receives a different input perturbation? How does the computational efficiency of parameter inference methods scale with the number of inputs and the number of measurement times? Are there approaches that are particularly well-suited for such data sets? In this paper, we investigate these questions using the CcaS/CcaR optogenetic system driving the expression of a fluorescent reporter protein as primary case study.

systems biology↗