Abstract
SummaryPlasmids are the workhorse of both industrial biotechnology and synthetic biology, but ensuring they remain in bacterial cells is a challenge. Antibiotic selection cannot be used to stabilize plasmids in most real-world applications, and inserting dynamical gene networks into the genome remains challenging. Plasmids have evolved several mechanisms for stability, one of which, post-segregational killing (PSK), ensures that plasmid-free cells do not survive. Here we demonstrate the plasmid-stabilizing capabilities of the axe/txe toxin-antitoxin system and the microcin-V bacteriocin system in the probiotic bacteria Escherichia coli Nissle 1917 and show that they can outperform the commonly used hok/sok. Using plasmid stability assays, automated flow cytometry analysis, mathematical models, and Bayesian statistics we quantified plasmid stability in vitro. Furthermore, we used an in vivo mouse cancer model to demonstrate plasmid stability in a real-world therapeutic setting. These new PSK systems, plus the developed Bayesian methodology, will have wide applicability in clinical and industrial biotechnology.
Highlights
The genes comprising a synthetic circuit can be maintained in a host bacterium in two ways: on the chromosome of the organism or on extra-chromosomal material such as plasmids
Mathematical models of plasmid stability consisted of terms for a plasmid-bearing population and plasmid-free population, growing exponentially at different rates with a constant probability of plasmid loss from the plasmid-bearing population (Boe et al, 1987)
A more detailed model of plasmid loss was devised that takes into account the age distribution of bacteria within a population, the predictions produced were virtually indistinguishable from simpler models (Lau et al, 2013)
Summary
The genes comprising a synthetic circuit can be maintained in a host bacterium in two ways: on the chromosome of the organism or on extra-chromosomal material such as plasmids. Plasmids are a fundamental biological tool and have been widely used in molecular and cellular biology research, leading to a number of well-developed methods for their manipulation (Ellis et al, 2011; Casini et al, 2015). This ease of manipulation enables a level of modularity, which is one of the key engineering goals of synthetic biology (Andrianantoandro et al, 2006; Martinez-Garcia et al, 2014). Maintaining the presence of the engineered circuit within the bacterial population is fundamentally important in the design of a predictable synthetic biological system
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