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(BBSRC DTP) Novel antimicrobial discovery using high-throughput pathway assembly and robotics


Project Description

Recent advances in molecular biology, in particular our improved ability to read and (most importantly) write genomic sequences, have led to renewed excitement in the area of genetic engineering, i.e. Synthetic Biology. Synthetic Biology has high biotechnological potential. One of its most promising application areas is the generation of new high-value bioactive natural products, including antibiotics and anticancer drugs.
One approach to overcome the worldwide antimicrobial resistance crisis is to stay ahead of the resistance game by accelerating the discovery of novel antibiotics. Most commercial antibiotics were discovered in actinomycete species, but these organisms are difficult to manipulate and take long to grow and are thus not suitable for use in high-throughput pathway assembly and robotics.
We aim to harness synthetic biology for the discovery of novel antimicrobials. We have identified biosynthesis pathways which encode for a type I polyketide synthase (a class of enzymes responsible for a variety of antimicrobial and anticancer agents) which can be expressed in E. coli for the first time. This breakthrough will allow us to use E. coli as a host for the production and discovery of novel antibiotics, exploiting synthetic biology approaches such as high-throughput pathway assembly and robotics.

Aim:
1. Identify and express several minimal units required to produce the core type I polyketide structures in E. coli which are identified computationally from genome sequences and analyse the obtained chemical structure.

2. Diversify and modify the obtained chemical structures by a) using different starter units with branching and length to create diverse molecules; b) introducing modifying enzymes, e.g. hydrolase, reductase, P450, glycosylases, to obtain novel end compounds. Suitable modifying enzymes will be selected using computational analysis.

3. Develop high-throughput pathway assembly and analysis using robotics as well as a microfluidic picodroplet system. The analysis will be based on a targeted metabolomics strategy (mass spectroscopy) coupled to high-throughput automation.

4. Further redesign and refactor the created biosynthesis pathways using, for example, the CRISPR/Cas9 genome editing system. Improve enzyme activity using protein evolution. Determine the chemical structures of newly produced compounds using several analytical methods, including mass spectroscopy and NMR.

This project is ideal for bioanalytical, biotechnology and biochemistry students, with a strong interest in modern microbiology, synthetic biology and high-throughput robotic/automation techniques, and a willingness to learn the interdisciplinary skills required for postgenomic data generation and analysis.

http://synbiochem.co.uk/

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the BBSRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

• Cummings M, Peters AD, Whitehead GFS, Menon BRK, Micklefield J, Webb SJ, Takano E. Assembling a plug-and-play production line for combinatorial biosynthesis of aromatic polyketides in Escherichia coli. PLOS Biol (2019) 17 e3000347.
• Jervis A, Carbonell P, Taylor S, Sung R, Dunstan M, Robinson C, Breitling R, Takano E, Scrutton N, SelProm: a queryable and predictive expression vector selection tool for Escherichia coli. ACS Synth Biol (2019) 8:1478-1483
• Del Carratore F, Zych K, Cummings M, Takano E, Medema MH, Breitling R. Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters. Communications Biology (2019) 2:83.
• Hernandez Ortega A, Vinaixa M, Zebec Z, Takano E, and Scrutton N. A Toolbox for Diverse Oxyfunctionalisation of Monoterpenes. Scientific Reports (2018) ) 8:14396
• Girvan HM, Poddar H, McLean KJ, Nelson DR, Hollywood KA, Levy CW, Leys D, Munro AW. Structural and catalytic properties of the peroxygenase P450 enzyme CYP152K6 from Bacillus methanolicus. J Inorg Biochem. (2018) 188:18-28

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