How and why we chose our iGEM project.
Molecular biology workflow is significantly impacted by microbial dynamics, which itself is heavily dependent on cell growth rate. Whilst growth rate is a parameter that can be altered via changes to simple culturing conditions (e.g. temperature, nutrient composition and agitation), an automatic plasmid system in which the growth rate can be reliably predicted and instigated has not yet been developed. Such a system would have several useful applications including: the precise syphoning of metabolic flux away from growth and toward production of proteins or other biomolecules of interest; the accurate and programmable modulation of ratios of species within bacterial co-cultures; and as an artificial selection pressure to allow for the fitness of an arbitrary gene to be tied to the fitness of the whole cell in in vivo directed evolution efforts. Thus, our goal is to create a system that allows for Escherichia coli growth rate to be a genetically programmable parameter.
This work began with the characterisation of various growth-slowing (GS) genes from bacteriophage: gp0.7 [1] and gp2 [2] from T7 bacteriophage; gp79 [3] from phiEco32; gp104 [4] from 77; gp240 [5] from G1; Alc [6] and AsiA [7] from T4. In order to definitively characterise these GS genes, we developed a set of calibration plasmids which allowed for the mathematical linkage between inducer concentration, amount of GS gene expression and subsequently the degree to which growth rate is hindered. Therefore, the amount of growth-slowing becomes easily calculable as a function of: the promoter in use, the inducer concentration and the particular growth slower in use - thus allowing for growth conditions to be optimised before experimental work begins. Furthermore, in doing so we delineated a methodology that allows users to repeat this process for any desirable inducible promoter. Whilst our data is concerned with the commonplace pLac promoter (with IPTG inducibility), we are confident that the same methodology could be applied to a range of other promoters of various biosensing relevance. This will enable the user to select the desired rate of growth for their E. coli culture by choosing an appropriate GS-promoter combination.
We have also worked on developing biosensing promoters-GS systems (such as a butanol sensing biosensor- pBmo/BmoR couples to a GS) to allow for ligand dependent modulation of growth rate in E. coli. In the presence of a molecule, normally a transcription factor or protein causes activation/suppression of expression from the biosensing promoter. This coupling with a biosensor, inducible promoter or quorum sensing system is important for a lot of the applications we suggest of PARSE in the biology overview.
To complement our biological work and modelling, we have also been developing a multiplexed turbidostat reactor system purpose-built for growth characterisation under dynamic conditions. This hardware is envisioned to be deployed as part of growth modulation experiments at different scales, providing a simpler alternative to more complex and costly microplate spectrophotometers. In operation, the multiplexed turbidostat reactor system will allow for the simultaneous cultivation, real-time analysis and study of multiple microbial cultures at varying conditions, while maintaining their turbidity at a constant level. A Simulink model of the project was created to simulate and aid in specifying and validating the system requirements for a multiplexed turbidostat bioreactor. It subdivides and offers representations of the fundamental controls for each subsystem, mirroring the control systems within the physical bioreactor. In addition to the Simulink model, the process of prototyping the physical build of the modular, multiplexed turbidostat is presented, and the planned production state is exemplified with the high-fidelity design of a custom-made multiplexed pinch valve as part of the reactor's fluid control system.
[1] J. Michalewicz and A.W. Nicholson, “Molecular cloning and expression of the bacteriophage T7 0.7 Protein kinase gene”, Virology, vol. 186, no. 2, pp 452-462, February 1992, doi:10.1016/0042-6822(92)90010-M.
[2] J. Plessis et al, “Exploring the potential fo T7 bacteriphage protein Gp2 as a novel inhibitor of mycobacterial RNA polymerase”, Tuberculosis, vol. 106, no., pp 82-90, July 2017. doi:10.1016/j.tube.2017.07.004
[3] D. Savalia et al., “Genomic and Proteomic Analysis of phiEco32, a Novel Escherichia coli Bacteriophage,” Journal of Molecular Biology, vol. 377, no. 3, pp. 774–789, Mar. 2008, doi:10.1016/j.jmb.2007.12.077.
[4] J. Liu et al., “Antimicrobial drug discovery through bacteriophage genomics,” Nature Biotechnology, vol. 22, no. 2, pp. 185–191, Jan. 2004, doi:10.1038/nbt932.
[6] M. Kashlev, E. Nudler, A. Goldfarb, T. White, and E. Kutter, “Bacteriophage T4 Alc protein: A transcription termination factor sensing local modification of DNA,” Cell, vol. 75, no. 1, pp. 147–154, Oct. 1993, doi:10.1016/s0092-8674(05)80091-1.
[7] L. J. Lambert, Y. Wei, V. Schirf, Borries Demeler, and M. D. Werner, “T4 AsiA blocks DNA recognition by remodeling σ70 region 4,” The EMBO Journal, vol. 23, no. 15, pp. 2952–2962, Jul. 2004, doi:10.1038/sj.emboj.7600312.