Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype.

A "Holy Grail" in biology is to deduce how an organism will behave under different conditions (its phenotype) from knowledge of its genetic make-up and how its genes are expressed. This is not yet possible, but this proposal will move us towards this goal, using bacteria as a model system.

There are several reasons why we want to be able to do this. First, we want to understand disease-causing bacteria better, so as to protect both ourselves and our food against their harmful effects better than we can do at the moment. Second, we use bacteria a lot in industry and our ability to do this will improve if we can predict in detail how they will behave under industrial conditions. Third, as biology moves towards a more synthetic approach where organisms are engineered to have specific functions, we need to understand how they will survive and thrive in different conditions. This project focusses on bacteria that cause disease, but the methods that we will develop will be applicable in many other situations.

Animals, including humans, have many barriers against bacterial infection, but bacteria are resilient and adaptable and can evade some or all of these, and go on to cause disease. To understand how they are able to do this, we need to understand in much more detail the underlying biology of these organisms under the conditions that exist in our gut. Fortunately, novel methods have been devised that allow us to do this, and this proposal will apply these. For some years, we have been able to make mutations which prevent particular genes from working and use bacteria carrying these mutations to study which genes are needed for survival when bacteria are exposed to stress. We've also known how to study the way in which a particular gene is turned up or down as the external conditions change. But now, it is possible to take a very large mixture of bacteria, containing hundreds of thousands of different mutations, expose all these bacteria to many different stresses, and see how well each mutant survives each stress. This can be done in just a few experiments. We can also study how every single gene in the bacterium is responding to the stress over time, again in a few experiments. Furthermore, we can use this information to construct computer models of how all the genes which respond to the different stresses in the bacteria are connected together. This is like going from a list of addresses in a phone book to a complete map of the streets and houses in a town. The first maps that we construct using this method may not be completely correct, but we can use experiments to check the maps in detail, refining each region until it truly represents what goes on inside the bacterial cell. This is what we will do in this project. We will use the models constructed to make predictions about how bacteria will survive under different conditions, like in a particular part of the gut, and which genes will be important in helping them do this. We will specifically test our ability to make accurate predictions as part of this project. Ultimately, this should help us to predict the vulnerabilities of any pathogenic bacterium, and to use this knowledge to devise novel strategies to protect us from their potentially lethal effects.

Predicting phenotype from genotype is a long-term goal in biology, and we will use a systems biology approach to do this in a pathogenic strain of E. coli. This proposal will identify key networks needed for E. coli to survive the stresses which it encounters in the gut. Our approach has been validated by our work on acid stress, which found new aspects of this process in E. coli. The unique, powerful feature of this proposal is the use of network-inference strategies on a combination of both gene expression and gene fitness measurements. It addresses several key BBSRC strategic priorities including Animal Health, Healthy and Safe Food, and Systems Approaches to the Biosciences. We will use TraDIS which involves the use of a very high-density transposon library. Such libraries can be used to estimate relative fitness of all the mutants, following exposure to different growth regimes, using HTS to find the level of each mutant before and after growth. This provides a measure of the fitness index for each gene under each condition, which, combined with expression data, will enable the modelling of networks based on functional associations. We will use different stresses, relevant to gut passage, on a library provided by our industrial collaborators, and then use inference to identify critical networks responsive to these stresses. Modules, gene hubs and other topological features will be identified in the model. Mutations in key pathways will be constructed and analysed further. Data from these studies will be used to refine the networks and to enable predictions of phenotype based on gene expression data. Predictions will be tested, and the models iteratively made more robust, by analysis of single gene knockouts and by experiments in an artificial gut system. This approach will be generalisable to any pathogen, and to industrial micro-organisms and organisms produced using synthetic biology methods.

Our approach exactly fits the BBSRC description of Systems Biology, i.e, we aim to "discover new emergent properties that may arise from studying the system as a whole, leading to more rapid and deeper understanding of how the system is controlled and how it responds to external stimuli." As BBSRC point out, "...this level of understanding will greatly facilitate the future exploitation of biological systems", so this proposal has significant potential in the understanding and exploitation of micro-organisms. This proposal also fully addresses the BBSRC strategic priority of data-driven biology, which states that "Projects should focus on underpinning and enabling one of the BBSRC strategic research priorities (food security, industrial biotechnology, bioscience underpinning health) or have potential, generic utility across one or more broad areas of the biosciences". In particular, it meets the call for "projects which aim to "[capture] variation and [link] biological processes through to phenotypic traits".

Who may benefit from this research, and how will they benefit? In the short to mid-term, because we will work on pathogenic E. coli, the non-academic users who will gain most from our work are (a) researchers in institutes and government agencies with interest in aspects of microbiology which have an impact on farm animals and food safety, and (b) companies with an interest in animal health, including those developing new therapeutics that target specific pathways which may be important in bacterial growth during infection. This directly relates to the BBSRC strategy in Animal Health, which particularly requests applications in "multidisciplinary projects that ... exploit advances in laboratory ... or in silico approaches to improve understanding, at the cellular, individual animal or population levels, of the host-pathogen interface or its relationship with the host animal's environment." It also directly addresses BBSRC strategy in Healthy and Safe Food, which includes studies aimed at "reducing the incidence of key food-borne pathogens throughout the supply food chain", since a deeper understanding of how bacteria survive in the food chain will emerge from this research. Our studies will clarify aspects of bacterial growth and infection that are currently not understood and which may either be targeted by changes in practice or by development of new therapeuticals. They will also provide novel methods that researchers can apply to their organism/stress of interest. In the mid to longer term, companies using bacterial or eukaryotic cell culture for production, and developing new processes for synthetic biology, also stand to benefit, by using these novel methods. This fits with the BBSRC priority "New strategic approaches to industrial biotechnology", that specifically asks for projects "[involving] the application of systems and synthetic biology approaches to reach these goals". Ultimately the public will benefit through lowered risks of infection from foods, improved ways of tackling infections, and more effective industrial processes. Given the current public health burden of food-borne infections, and the threat of antibiotic resistance, these could be substantial benefits.

The timescale over which this impact might be delivered is hard to estimate but we plan some stakeholder engagement within the lifetime of the project (see "Pathways to Impact"). If we are successful in delivering the objectives of the project, we will seek further funding to move our work into a more applied field, both with regard to pathogens (animal and human) and industrial organisms.