Pharmacological, biological and mathematical expertise align to create the first fully tuneable G-protein-coupled receptor (GPCR) system; a living ‘mathematical model’ of a signalling pathway in yeast cells, published in Cell this week. Creating the system was an interdisciplinary effort that has a host of potential applications, including use as a highly sensitive biosensor, helping to better understand why some patients have a better response to medicines than others or even having the ability in future to control synthetic cell systems as cell therapies
AstraZeneca’s Mark Wigglesworth, Director in Discovery Sciences, talks to his co-authors, Dr Tom Ellis, a Reader at the Faculty of Engineering, Imperial College London, and Dr Graham Ladds, a Lecturer in the Department of Pharmacology, University of Cambridge, about this published research – its background, findings and implications.
This project shows the benefits of drawing on connections within and across fields, as well as bringing complementary expertise into a project at the right time in order to elevate the science to a much higher calibre.
Mark Wigglesworth: To introduce the background to this research – I have a long-standing interest in synthetic biology and GPCRs, and a few years ago I invited Tom to give a talk at AstraZeneca on this topic. One of our students on our MSc programme, William Shaw (the first author on this paper), was in the audience. After Tom’s talk, Will came to me and said he was inspired to do a PhD around what Tom had presented. We discussed ideas and I reached out to Tom to make the connection. At the time our interest was focused on how we could sense and respond appropriately to generate cell systems that had the potential to modify disease. Will, Tom and I then successfully applied for a CASE studentship (funding for a collaborative academia–industry placement) for Will to undertake his PhD in Tom’s lab at Imperial.
Mark: Tom, can you tell us about the ideas that led to the project we have now?
Tom Ellis: We know GPCR systems are used to sense most things in human cells – and we thought we could take them outside of the body and use them as biological sensors (or ‘biosensors’). Harnessing this type of natural engineering could allow us to create systems with capabilities that would be difficult to replicate with something synthetic.
We started out by looking at which genes to knock down to isolate one GPCR pathway in yeast. We developed a toolset with CRISPR reagents to edit key genes; we wanted to delete any unwanted interactions outside the pathway of interest that could distort the picture.
It became apparent that having mathematical models would help us identify and target the parts of the pathway we needed to knock out. This was when you suggested bringing Graham on board.
Mark: Graham, please could you tell us about your part in this project?
Graham Ladds: My lab does mathematical modelling, and I also have experience expressing human receptors in yeast.
Tom and Will were trying to look at components that effected the cellular responses they are interested in. This is much quicker and easier to do using computational models than it is to do gene editing, so we used the models to find ‘sweet spots’ in the pathway to edit, before making the changes in vivo. We had previously created models of signalling cascades in yeasts, then we trained those models into Tom and Will’s system.
This research truly was a two-way process, where the mathematical and biological data helped to refine each other.
Mark: And it’s correct to say that in addition to the mathematical models supporting the biological data, the biological data also helped to refine the mathematical models?
Graham: Yes. Tom and Will could knock out genes in vivo based on what our model predicted would happen, and come back with results and see how these fit back into the model. This information based on what we observed in vivo was fed into our models to further improve them. This research truly was a two-way process, where the mathematical and biological data helped to refine each other.
Mark: And briefly, what were the key findings?
Tom: The key scientific finding was that there are key components in a GPCR signal transduction that can very reliably predict how the cell responds to a signal that it detects via a GPCR.
Using a combination of mathematical modelling work and experimental work to create a highly-engineered cell, we were able to demonstrate almost all the characteristics of how a cell responds to a molecule – including the cell can be tuned to respond to a specific concentration of the molecules that activated it, by changing the levels of a few proteins in the pathway.
Graham: What we have created now is live yeast, with one, isolated GPCR signalling pathway. This is the first such system of its kind, and combines the advantages of both in vivo and computational systems. We have complete control over every component as we do in a computational system, but everything we observe is happening in a living system.
Quite simply, the project would not exist for any of these organisations individually. Synthetic and pharmacological capability and experience have come together with mathematical modelling to make this.
Mark: It’s very exciting. What do you see as the potential future directions for this type of work?
Tom: It is indeed very exciting, and this kind of system has broader biomedical implications and applications. In theory, we could define the sensitivity of these receptors by modulating parts of the signalling pathway, so they detect specific concentrations of molecules we are interested in.
Being able to examine the GPCR pathway at every stage could also allow us to better understand how patients respond to medicines, and why some patients respond better than others, despite appearing to have similar expression of receptors – it is likely to be due to differences downstream. GPCR signalling is involved in a lot of disease pathways, and therefore a lot of drugs act on GPCRs.
There are also gene therapy implications, where mutations and non-coding genetic aspects that may be present in an individual’s genome could be targeted and help to refine precision medicine even further. This potential application could help build a better picture of which GPCR-related drugs may be appropriate for a certain individual, but this is much further down the line.
Graham: In the future, the work could involve looking at more complex, multicellular systems, including mammalian cells. We do realise this will be a complex task, purely based on how these cells interact within their environment. However, that's not going to stop us trying to recreate this work in human cells.
Mark: Collaboration often allows you to build something that’s greater than the sum of its parts, doesn’t it?
Graham: I love interdisciplinary research. Working with other people keeps your horizons broad. In this case, we all know what our skills are, and we can put our expertise together to make a really powerful package. I don’t think we would be discussing a Cell paper if we hadn’t come together to do this.
Tom: Quite simply, the project would not exist for any of these organisations individually. Synthetic and pharmacological capability and experience have come together with mathematical modelling to make this. And it also wouldn’t have happened if it weren’t for Will Shaw, who I want to thank for being such a dedicated and excellent student.
Mark: Thank you both very much for your time and discussing this exciting work with me. This project goes to show the benefits of drawing on connections within and across fields, as well as bringing complementary expertise into a project at the right time in order to elevate the science to a much higher calibre. It also sows the seeds for potential new treatment advancements.