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

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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 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.