Project overview

All bacterial cells are surrounded by a macromolecule called peptidoglycan that underpins cell viability and confers cell shape and resistance to mechanical stress. During growth, “peptidoglycan remodelling” allows bacteria to adapt to changing environmental conditions and is important for bacterial population dynamics. Peptidoglycan is important for microbe-host interactions. Some fragments contribute to diseases caused by pathogens whilst other fragments released by probiotics have a beneficial role and enhance antitumor cancer immunotherapy. Peptidoglycan fragments produced by gut bacteria can circulate in the organism and can be sensed by the brain to modulate behaviour sleep and appetite.

The aim of this proposal is to build an integrated software platform to analyse the composition and structure of peptidoglycan. The analysis pipeline will allow users with no expertise in computing science to analyse large datasets generated by liquid chromatography coupled to mass spectrometry. The integrated suite of open-source software we propose to create will be available through a web interface. This new toolbox represents a transformative step for the consistent, automated, and reproducible analysis of bacterial peptidoglycans to investigate the contribution of this molecule to antimicrobial resistance, disease and neurodevelopmental disorders.

Objectives

We propose to create a transformative, versatile open-source software toolbox to make “peptidoglycomics” a full-fledged discipline. The objective is to build a user-friendly and open-source software platform that will be available through the Glyco@expasy website. The corresponding tools will enable users with no prior expertise in PG structural analysis or computer science to perform a consistent unbiased analysis of peptidoglycan structure by mass spectrometry.

Novelty

There is currently no software tool to study bacterial peptidoglycans. The transformative resource we want to build will therefore fill a gap in our knowledge, allowing us to explore the so-called “PG dark matter” and the role of PG as a signalling molecule in the context of complex polymicrobial systems like in the gut, the oral cavity, or the soil microbiome. Our software suite will also be applicable to a wide range of molecules, including metabolites and unusually crosslinked molecules such as antibodies or cyclic peptides.

Timeliness

We will build on our recent software called PGFinder dedicated to PG analysis (https://github.com/Mesnage-Org/pgfinder; Patel et al., Elife, 2021) created in collaboration with the RSE team (TUoS). This work will feed into existing projects in the supervisor’s lab focusing on PG remodelling during bacterial growth and differentiation.

Experimental Approach

The project is multidisciplinary by nature, involving computing sciences, analytical biochemistry, and data analysis. Core software modules will be created as Python packages built into Docker containers. The web application will consist of a backend part, written using a standard Python web framework, and a frontend part (User Interface), written using JavaScript framework. LC-MS/MS data will be generated using by preparing material from bacteria grown in vitro or using biological material provided by collaborators. The datasets generated will enable software beta-testing and optimization of the tools created.

The project is multidisciplinary by nature and will be co-supervised by a team with expertise in software development and microbiology. The student will acquire skills in computing sciences (python programming, web development) and biological sciences (microbiology, cell wall biochemistry and mass spectrometry analysis). The project is suitable for a student with knowledge in computing science and no prior expertise in biology.

Essential Criteria

• Prior experience of coding language (Python, R, C++)

Desirable Criteria

•  Experience in Web-development
•  Basic knowledge in biology

For informal contact, please get in touch with S. Mesnage ([email protected]; https://mesnagelab.weebly.com/) or Neil Shephard ([email protected]; https://ns-rse.github.io/)

Please refer to the EPSRC DTP webpage for detailed information about the EPSRC DTP and how to apply.

For any queries about the project please contact Dr S Mesnage.