Development of a Systems Biology for Bordetella pertussis Metabolism

Lead Research Organisation: Imperial College London
Department Name: Dept of Mathematics


Context Microbial metabolism consists of networks of reactions that bacteria use to convert nutrients into molecules that make up the cell and release energy for cell processes. Such networks have an enormous potential to produce a huge range of molecules that might be useful, for example as biofuels or biopharmaceuticals, but these are rarely produced at high levels. Genome sequencing has revealed metabolic pathways that produce these molecules, and genetic modifications of these pathways allow us to manipulate microbes for biotechnology purposes. However, the high complexity of bacterial metabolism has considerably limited such efforts. This calls for new approaches that can incorporate the complexity of metabolic systems and predict appropriate modifications. Aims We will develop novel computational approaches to modelling microbial metabolism, and use the results to optimise growth in the laboratory. When developed in close collaboration with experimentalists, models can effectively guide experiment design, and models are also safe and inexpensive to work with. Computational models are increasingly important tools to analyse complex interaction networks, as they can incorporate many interactions in large complex systems. We will develop models representing metabolic network,s and algorithms to analyse these and direct experiments towards optimal modifications to improve growth. The underlying model will use genome sequence information and gene expression data to determine the enzymes that are present in the bacterium and thus which reactions are operating under different conditions. Based on this, together with data we collect in the laboratory, we will predict and test ways to optimise the metabolic network, for example to find nutrient conditions that permit the most growth on the least expensive medium. We will use the bacterium Bordetella pertussis as a model system. The relevant genome sequence and gene expression data are available, and B. pertussis has an intriguing metabolism and will provide a different perspective from previous method development work, which has focussed largely on E coli, although the methods themselves have been applied in other organisms. We will develop optimisation algorithms to predict ways to improve the growth of B. pertussis either through altered growth media or by genetic alterations to enhance growth. We will test these predictions in laboratory experiments to validate and refine the novel methods we develop, and to develop its applications. The combination of theoretical modelling with experimental testing is a powerful approach that is superior to purely theoretical systems. Applications and benefits This proposal will develop new methods to use genome sequence information and gene expression data in computational models of microbial metabolism. The cost of whole genome sequencing is dropping rapidly while the capacity of genome sequencing centers to generate data is rapidly increasing. Thus new approaches to interpret and exploit genomic data are needed urgently. The move away from single-gene studies towards genome level studies facilitates a more holistic view of an organism than before and motivates genome-scale, systems-based research approaches. The concepts and approaches developed in this proposal will thus be widely applicable to other studies using genome sequence data. The data generated will also be widely usable. Metabolism is fundamental to the physiology of all bacteria, so the wider perspective of metabolism gained by our studies is of interest to a broad audience. Improved growth methods for B. pertussis will be valuable to sectors of the biotechnology industry that grow this bacterium on a large scale, such as vaccine manufacturers. Thus, although we are using B. pertussis as a model organism for the development of novel systems biology methods, this will generate outputs that have immediate impact outside of academia.

Technical Summary

Our objectives are (1) to develop systems-based tools to manipulate bacterial metabolism, consisting of validated methods to optimise nutrient conditions and (2) to thereby make genetic modifications to improve growth. Methods will be based on flux balance analysis (FBA), a modelling approach that uses the rapidity of enzymatic transients to model metabolism at steady state. Reactions are encoded in a stoichiometric matrix S; metabolites correspond to rows and reactions to columns. If v is a vector of fluxes, Sv=0 defines a steady state, with no net production or consumption of internal metabolites. The FBA approach uses linear programming to find a set of fluxes that optimise production of a biomass objective, which we will construct using measurements of DNA, carbohydrate, lipid and protein content of dry cell mass. We will construct an FBA model for B. pertussis and curate it by comparing its growth parameters to measurements in the chemostat system, and by comparing in silico gene knockout predictions to gene essentiality data being generated at the Sanger Centre. We will develop and test two central computational tools: one that connects microarray mRNA expression data to the FBA model by linking measured expression with flux constraints in the linear program, and one that decomposes flux vectors into elementary modes, which are minimal sets of reactions capable of functioning at steady state. We will develop novel systems-based methods to predict optimal growth-per-cost nutrient conditions and key genes to knock out or overexpress to improve growth. Genetic modifications will be performed by mutating genes (knockouts) and plasmid carriage and transfer, or cloning behind relevant promoters (overexpression). The end result will be systems biology tools enabling the manipulation of Bordetella pertussis metabolism, with applications in other organisms and in a range of bioproduction settings.

Planned Impact

This proposal will develop novel methods in systems biology approaches to microbial metabolism. This cutting-edge approach is an area of research for numerous other investigators, who will directly benefit from the publication of our methodologies and findings. We will also generate novel data regarding microbial metabolism, a fundamental aspect of the physiology of all bacteria. Our findings will thus directly impact the broader microbiology research community. In particular, the detailed understanding of how B. pertussis metabolises nutrients to generate energy and biomass will inform the studies of other Bordetella researchers, and the improved culture methods we develop will greatly benefit their work. The enhancement of other researcher's programmes within the UK will increase their ability to obtain funding, including international funding. Dissemination of our methods and findings through publications and conference presentations will ensure that a global audience can benefit from our work, and do so during the project's lifetime. B. pertussis causes whooping cough, a serious infectious disease worldwide; vaccination against this bacterium is part of the standard childhood vaccination programmes in many parts of the world. Thus, many millions of doses of vaccine are produced each year. The slow and often variable growth of B. pertussis in culture is a major issue for vaccine manufacturers. The improved growth characteristics resulting from this project would be a great boost to the efficiency of vaccine production, and would benefit a number of private sector companies. Possible commercial exploitation of our findings will be investigated through discussions between the Technology Transfer Office at the University of Bristol and R&D managers of whooping cough vaccine manufacturers. In turn, improved vaccine production paves the way for cheaper whooping cough vaccines globally, with particular impact in the developing world. Cheaper vaccines would also generate savings to the NHS, which administers the statutory childhood vaccination programme in the UK . This currently includes three pertussis immunizations during early childhood and a school age booster vaccination. It is anticipated that enhanced growth methodologies can be transferred to commercial settings in the short term. Clinical diagnosis protocols for whooping cough still regard culture of B. pertussis as the gold standard, but it is time-consuming and has poor sensitivity compared to PCR. Faster and more robust growth of B. pertussis will help to address both of these constraints. Public health organizations involved in clinical diagnosis of B. pertussis infections will directly benefit from this work and in turn, the general public will benefit from the enhanced diagnostic capabilities. The development of novel diagnostic protocols will be explored with staff at the Respiratory Infections Lab at the Health Protection Agency. We will train two researchers in the application of computational models to the rational design of microbial metabolism. These researchers will be well placed to conduct further systems biology research either in academia or industry. Rational design of bioproduction processes is regarded as a hugely important direction for a wide range of biotechnology sectors including production of biofuels, vaccines and antibiotics. A workforce with training in these disciplines is vital for the UKs ability to engage these new approaches. In addition this work will aid in the training of our jointly funded PhD student, and will also have impact in our undergraduate teaching. Our work involves aspects of both systems and synthetic biology, and as such fits the priorities of the Research Councils and is of interest to the general public. Together with the Synthetic Components Network and the University's Centre for Public Engagement we are pursuing a range of engagement activities including public talks, Science Cafes and school visits.


10 25 50

Related Projects

Project Reference Relationship Related To Start End Award Value
BB/I00713X/1 11/07/2011 04/10/2011 £576,513
BB/I00713X/2 Transfer BB/I00713X/1 05/10/2011 04/01/2015 £555,034
Description We have found that there are simple ways to compare the growth of B. pertussis in its two main contrasting modes (slow and fast), which help explain why growth is so different. We have developed key methods to move the systems biology of this organism forward: a computational model of its metabolism, which we have curated, and algorithms to incorporate additional data.
We have tested the model with reference to TRADIS gene essentiality data and used these data for validation. We have also designed a variant of flux balance analysis that copes elegantly with gene complexes. We have further validated the model by testing its predictions about growth on different media, and had some very positive results. GSK is pursuing using the model to optimise growth media for pertussis in vaccine production.
We have develop an algorithm to constrain flux balance models in a gene-centric way. In contrast, standard approaches effectively assume that a protein can be in two places at once. Instead, we model a combined constraint on all reactions catalysed by a protein. We have made the method available to the community as a python module. A key advantage of our approach is that it can be applied to metagenomic data.
Exploitation Route We provide the computational models and methods we have developed, which are widely applicable to other organisms.
Sectors Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology

Description GSK is using some of our results in optimising the nutrient medium for Bordetella pertussis growth.
First Year Of Impact 2014
Sector Pharmaceuticals and Medical Biotechnology
Impact Types Economic

Description Developing the Bvg minus phase of Bordetella pertussis as a vaccine platform
Amount £93,520 (GBP)
Funding ID BB/K011642/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 10/2013 
End 09/2017
Title Metabolic model of Bordetella pertussis metabolism 
Description This is the first curated genome-scale metabolic model of B. pertussis. 
Type Of Material Computer model/algorithm 
Provided To Others? No  
Impact This enables metabolic analysis of pertussis transcriptomic data as well as nutrient use. Publications are pending and the model will be released publicly shortly. 
Title Pipe-based flux analysis 
Description We developed a new way to link transcriptomic data to flux balance models, which captures the role of a gene in multiple reactions in a new way. 
Type Of Material Computer model/algorithm 
Provided To Others? No  
Impact We, and others, will be able to better interpret transcriptomic data in light of metabolism. 
Description GSK 
Organisation GlaxoSmithKline (GSK)
Country Global 
Sector Private 
PI Contribution We provided modelling methods and advice as well as microbiology expertise.
Collaborator Contribution They provided their in-house metabolic model and details of their proprietary approaches. We now share a BBSRC CASE award.
Impact BBSRC Case studentship (A. Preston at Bath is the PI). Multi-discplinary: applied mathematics; microbiology.
Start Year 2012
Description Metabolic modelling group 
Organisation Imperial College London
Country United Kingdom 
Sector Academic/University 
PI Contribution We collaborated on metabolic modelling, providing a test model for B pertussis.
Collaborator Contribution They provided algorithms for visualisation and analysis in support of model development.
Impact We received internal funding from Imperial to pilot a web server for SBML metabolic model curation. We will likely apply for follow-on funding to address critical bottlenecks in the field.
Start Year 2012
Title GCFlux 
Description GCFlux is a python module for gene centric constrains on metabolic flux models. 
Type Of Technology Software 
Year Produced 2015 
Open Source License? Yes  
Impact Impact has not yet occurred but this approach makes constraint-based modelling tools available in metagenomics and we anticipate that it may be widely used and tailored to individual datasets.