Award details

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

Reference	BB/K019546/1
Principal Investigator / Supervisor	Professor Francesco Falciani
Co-Investigators / Co-Supervisors
Institution	University of Liverpool
Department	Institute of Integrative Biology
Funding type	Research
Value (£)	260,961
Status	Completed
Type	Research Grant
Start date	01/12/2013
End date	30/11/2016
Duration	36 months

Abstract

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.

Summary

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.

Impact Summary

Our ultimate goal is a truly predictive biology, where the fitness of an organism in a particular environment can be accurately predicted from a detailed knowledge of its molecular state. The ability to do this is relevant to many areas of BBSRC funded research. The current proposal will move us towards this goal by developing novel computational models, reflecting the structure of the underlying biological networks, and predictive of the phenotypic responses to a range of stresses relevant to the survival of a food-borne pathogen. These models will simulate molecular adaptation and predict fitness in different environments both in the laboratory and in an artificial gut model. All predictions will be experimentally tested. The methods developed will be highly generalisable, for example to bacteria growing in an industrial fermentation. Our models will allow considerable advances in the understanding of bacterial adaptation to stress, particularly by identifying regulatory circuits that allow survival in different stress conditions. The ability to predictively link pathways, fitness, and phenotype will also be essential in the application of synthetic biology, for example in the construction of organisms with improved ability to perform in unstable conditions. Our model organism will be E. coli. A known pathogenic strain (UO399, an important multi-drug resistant isolate) will be used, so that the data are representative of a pathogen and not a laboratory-adapted strain. We will use proven computational methods to infer the regulatory networks that enable E. coli to respond to a variety of stresses, including some that it normally encounters in the mammalian gut. The data used for this analysis will be generated under a range of physiologically relevant stress conditions, using approaches based on high throughput sequencing, and will consist of measures of relative fitness for all non-essential genes and gene expression data. This will enable the identification of key regulatory networks and pathways underlying stress survival. The expression profiles that result will be analysed so that specific phenotypic outcomes become predictable from expression data. The hypotheses generated by these models will be tested in the laboratory using a combination of genetics and molecular methods. In addition, the relevance of the system in a complex scenario will be tested by using a validated artificial gut model. These objectives are relevant to BBSRC strategic priorities in Animal Health, Healthy and Safe Food, and (in particular) Synthetic Biology and Systems Approaches to the Biosciences. The specific experimental objectives are as follows. 1) Determination of a gene fitness index for each non-essential gene, and acquisition of expression data, for all the genes of UO399. This will be done under a range of conditions representative of stresses that are encountered during passage through the mammalian gut. Stresses will be applied under both aerobic and anaerobic growth conditions. 2) Inference of networks that are critical for survival of individual or combined stresses, by combining models generated from the gene fitness measures and gene expression data, plus integration of data which is already available, and prediction of key genes and pathways in each stress. 3) Testing of predictions from inferred network models, by generation of specific mutants and measurement of fitness by competition experiments under laboratory conditions. 4) Use of the models to make and test predictions about gene fitness in an artificial gut model, based on gene expression data obtained in that model. 5) Testing the extent to which the models and predictions can be generalised across different E. coli strains. Objectives (2) to (5) will be met through an iterative process of data generation, modelling, prediction, and testing.

Committee	Research Committee B (Plants, microbes, food & sustainability)
Research Topics	Microbial Food Safety, Microbiology, Systems Biology
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

Associated awards:

BB/K017373/1 Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype

BB/K019171/1 Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype.

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