A genetic toolkit and gene switches to limit Mycoplasma growth for a synthetic vaccine chassis

Abstract

Mycoplasmas have exceptionally streamlined genomes and are strongly adapted to their many hosts, which provide them with essential nutrients. Owing to their relative genomic simplicity, Mycoplasmas have been used for the development of chassis to deploy tailored vaccines. However, the dearth of robust and precise toolkits for genomic manipulation and tight regulation has hindered any substantial advance. Herein we describe the construction of a robust genetic toolkit for M. pneumoniae, and its successful deployment to engineer synthetic gene switches that control and limit Mycoplasma growth, for biosafety containment applications. We found these synthetic gene circuits to be stable and robust in the long-term, in the context of a minimal cell. With this work, we lay a foundation to develop viable and robust biosafety systems to exploit a synthetic Mycoplasma chassis for live attenuated vaccines or even for live vectors for biotherapeutics.

Modelling genetic stability in engineered cell populations

“…Finally, while our model is parameterised based on E. coli , it could be adapted to other bacterial hosts by using alternative parameter values. This would be useful when applying resource constraints to organisms that have different industrial applications, for example Pseudomonas [55, 56] and M. genetalium [57]. Modelling eukaryotic chassis may be more difficult due to their added complexity, however a recent model by Frei et al [58] showcases a predictive cell model that successfully captures resource allocation in mammalian cells.…”

When synthetic biology fails: a modular framework for predicting genetic stability in engineered cell populations

“…Finally, while our model is parameterised based on E. coli, it could be adapted to other bacterial hosts by using alternative parameter values. This would be useful when applying resource constraints to organisms that have different industrial applications, for example Pseudomonas [55,56] and M. genetalium [57]. Modelling eukaryotic chassis may be more difficult due to their added complexity, however a recent model by Frei et al [58] showcases a predictive cell model that successfully captures resource allocation in mammalian cells.…”