TY - JOUR AB - In Gram negative bacteria, the multiple antibiotic resistance or mar operon, is known to control the expression of multi-drug efflux genes that protect bacteria from a wide range of drugs. As many different chemical compounds can induce this operon, identifying the parameters that govern the dynamics of its induction is crucial to better characterize the processes of tolerance and resistance. Most experiments have assumed that the properties of the mar transcriptional network can be inferred from population measurements. However, measurements from an asynchronous population of cells can mask underlying phenotypic variations of single cells. We monitored the activity of the mar promoter in single Escherichia coli cells in linear micro-colonies and established that the response to a steady level of inducer was most heterogeneous within individual colonies for an intermediate value of inducer. Specifically, sub-lineages defined by contiguous daughter-cells exhibited similar promoter activity, whereas activity was greatly variable between different sub-lineages. Specific sub-trees of uniform promoter activity persisted over several generations. Statistical analyses of the lineages suggest that the presence of these sub-trees is the signature of an inducible memory of the promoter state that is transmitted from mother to daughter cells. This single-cell study reveals that the degree of epigenetic inheritance changes as a function of inducer concentration, suggesting that phenotypic inheritance may be an inducible phenotype. AU - Guet, Calin C AU - Bruneaux, L AU - Oikonomou, P AU - Aldana, M AU - Cluzel, P ID - 12478 JF - Frontiers in Microbiology TI - Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression VL - 14 ER - TY - JOUR AB - 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. AU - Davidović, Anđela AU - Chait, Remy P AU - Batt, Gregory AU - Ruess, Jakob ID - 10939 IS - 3 JF - PLoS Computational Biology SN - 1553-734X TI - Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level VL - 18 ER - TY - JOUR AB - Objective: MazF is a sequence-specific endoribonuclease-toxin of the MazEF toxin–antitoxin system. MazF cleaves single-stranded ribonucleic acid (RNA) regions at adenine–cytosine–adenine (ACA) sequences in the bacterium Escherichia coli. The MazEF system has been used in various biotechnology and synthetic biology applications. In this study, we infer how ectopic mazF overexpression affects production of heterologous proteins. To this end, we quantified the levels of fluorescent proteins expressed in E. coli from reporters translated from the ACA-containing or ACA-less messenger RNAs (mRNAs). Additionally, we addressed the impact of the 5′-untranslated region of these reporter mRNAs under the same conditions by comparing expression from mRNAs that comprise (canonical mRNA) or lack this region (leaderless mRNA). Results: Flow cytometry analysis indicates that during mazF overexpression, fluorescent proteins are translated from the canonical as well as leaderless mRNAs. Our analysis further indicates that longer mazF overexpression generally increases the concentration of fluorescent proteins translated from ACA-less mRNAs, however it also substantially increases bacterial population heterogeneity. Finally, our results suggest that the strength and duration of mazF overexpression should be optimized for each experimental setup, to maximize the heterologous protein production and minimize the amount of phenotypic heterogeneity in bacterial populations, which is unfavorable in biotechnological processes. AU - Nikolic, Nela AU - Sauert, Martina AU - Albanese, Tanino G. AU - Moll, Isabella ID - 11713 JF - BMC Research Notes KW - General Biochemistry KW - Genetics and Molecular Biology KW - General Medicine SN - 1756-0500 TI - Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli VL - 15 ER - TY - JOUR AB - Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought. AU - Lagator, Mato AU - Sarikas, Srdjan AU - Steinrueck, Magdalena AU - Toledo-Aparicio, David AU - Bollback, Jonathan P AU - Guet, Calin C AU - Tkačik, Gašper ID - 10736 JF - eLife TI - Predicting bacterial promoter function and evolution from random sequences VL - 11 ER - TY - JOUR AB - Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy. AU - Römhild, Roderich AU - Bollenbach, Mark Tobias AU - Andersson, Dan I. ID - 10812 JF - Nature Reviews Microbiology KW - General Immunology and Microbiology KW - Microbiology KW - Infectious Diseases SN - 1740-1526 TI - The physiology and genetics of bacterial responses to antibiotic combinations VL - 20 ER -