Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype.
Lead Research Organisation:
University of Birmingham
Department Name: Sch of Biosciences
Abstract
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.
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.
Technical Summary
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.
Planned Impact
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.
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.
Publications
Tonner PD
(2020)
A Bayesian non-parametric mixed-effects model of microbial growth curves.
in PLoS computational biology
Lund P
(2014)
Coping with low pH: molecular strategies in neutralophilic bacteria.
in FEMS microbiology reviews
Sannasiddappa TH
(2017)
In Vitro Antibacterial Activity of Unconjugated and Conjugated Bile Salts on Staphylococcus aureus.
in Frontiers in microbiology
Bushell FML
(2018)
Synergistic Impacts of Organic Acids and pH on Growth of Pseudomonas aeruginosa: A Comparison of Parametric and Bayesian Non-parametric Methods to Model Growth.
in Frontiers in microbiology
De Biase D
(2015)
The Escherichia coli Acid Stress Response and Its Significance for Pathogenesis.
in Advances in applied microbiology
Goodall ECA
(2018)
The Essential Genome of Escherichia coli K-12.
in mBio
Description | We (together with our collaborators in Liverpool) have generated a very large dataset of RNAseq and traDIS data under wide range of different stress conditions for the uropathogenic strain of E coli ST131. These include altered pH, different organic acids, bile salts, osmotic sterss, oxidative stress, altered carbon sources, all done under both aerobic and anaerobic conditions. We believe this is probably the largest extant traDIS dataset for E coli, it is certainly larger than anything in the current literature. Analysis of the datasets and the relationships between them, and experiments to test the predictions made from these analyses, are still ongoing, and the analysis already completed has indicated key genes for mutagenesis and testing. The Liverpool team has built a network model of the transcriptomics and fitness data and then shown that the early transcriptional response to stress is predictive of later fitness. Interestingly these models have led to the identification of several putative targets that we are now still testing. Unfortunately, the difficulties in generating the mutant strains has been slowed down delivery of all objectives of the project. The Liverpool team has refined the computational models and focussed on the analysis of the transcriptional response in relation to fitness. More precisely, they have asked whether, genes that are modulated in the first 15 minutes of the exposure to the stressor are indeed contributing to survival. They have addressed this important question by directly comparing gene expression with the traDIS fitness data. Interestingly, this shows that E. coli ST131 transcriptional response to the stressor can be subdivided in a pro-survival response and an anti-survival response that we can associate to biological pathways that lead to an adverse survival outcome. We are currently preparing a joint publication outlining these results that we expect to publish in PLOS Computational Biology. |
Exploitation Route | Use of methods (to be published) for analysis of large datasets of RNAseq/traDIS data; analysis of predicted phenotypes of a range of mutant strains |
Sectors | Agriculture, Food and Drink,Healthcare,Manufacturing, including Industrial Biotechology |
Description | Development of new method for incubating bacteria in artifical gut model |
First Year Of Impact | 2014 |
Impact Types | Cultural |
Description | Member, Scientific Advisory Committee on Genetic Manipulation |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | I was appointed as a cross-committee member of the HSE advisory committee SACGM in 2015 (I already hold an appointment on the DEFRA advisory committee ACRE, with expertise in synthetic biology. The committee has had to consider issues such as where newly engineered organisms fit under the regulatory regime and what sort of data is needed to evaluate any hazards that might be associated with them and I have been actively involved in discussions around this issue. |
URL | http://www.hse.gov.uk/biosafety/gmo/acgm/acgmcomp/ |
Description | Membership of Advisory Committee on Releases to the Envirnment (DEFRA) |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | As member of this committee I have been closely involved with discussions on topics such as how synthetic biology should be regulated, regulations of organisms with genome engineered via CRISPR/cas9 and related systems, regulation of GMOS that are being used as therapeutic agents, and I also provide cross-committee expertise with the HSE committee SACGM. |
Description | BBSRC Midlands Integrative Biosciences Training Partnership |
Amount | £10,500 (GBP) |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2017 |
End | 08/2020 |
Description | Government of Kuwait PhD scholarship scheme |
Amount | £24,000 (GBP) |
Organisation | Government of Kuwait |
Sector | Public |
Country | Kuwait |
Start | 08/2016 |
End | 08/2019 |
Title | Method for isolation of transposon mutants from a complex transposon library |
Description | Optimisation of use of nested PCR and PCR on pooled samples to identify rare individual mutants within a large (3,000,000 member) transposon library. |
Type Of Material | Biological samples |
Year Produced | 2018 |
Provided To Others? | No |
Impact | Not yet published, |
Description | Collaboration with University of Liverpool |
Organisation | University of Liverpool |
Department | Institute of Integrative Biology |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Provision of experimental samples |
Collaborator Contribution | Generation of sequence reads for both RNAseq and traDIS, analysis of reads |
Impact | Collaboration between us (experimental biologists), University of Liverpool sequencing service (high throughput RNA and DNA sequencing), and bioinformatics team (Institute of Integrative Biology). |
Start Year | 2014 |
Description | Collaboration with University of Reading |
Organisation | University of Reading |
Department | School of Agriculture, Policy and Development Reading |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Manpower and experimental samples |
Collaborator Contribution | Provision of artifical gut model facilities and expertise at University of Reading |
Impact | Set of data on responses of bacteria to growth in spent medium from artifical gut system ; paper in preparation |
Start Year | 2014 |
Description | Identification of genes required for, or involved in resistance to, a novel antimicrobial compound |
Organisation | University of Liverpool |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Used methods arising from the BBSRC award on "Towards predictive biology: using stress responses in a bacterial pathogen to link molecular state to phenotype". Compound was provided by a company (Auspherix). We used traDIS methods with different concentrations of the compound to identify (a) genes which when mutated led to reduced growth at sub-mic concentrations and (b) genes which when mutated led to enhanced growth at sub-mic and greater than mic concentrations. Work was done by BBSRC-funded student whose project arose directly from the initial BBSRC award. |
Collaborator Contribution | Provided lab space, compound, and bioinformatics expertise. Research was funded by University of Liverpool spinout company. |
Impact | None to date but publication is expected as a specific finding has been made. Work is ongoing in developing and refining methods to link traDIS and whole genome sequence data from lab-evolved organisms with a view to enabling rapid prediction of key genes involved in novel resistance to antimicrobials. |
Start Year | 2018 |
Description | Member of organising and scientific committees, EFB conference on Microbial Stress, Sitges, Spain |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Conference was third in a series of European conferences which I set up to bring together academics and industrial researchers interested in stress responses in micro-organisms. Attended by approx 120 people from post-grad to professorial level. Led to a conference to be run this year (2018), a COST application to EU, and a ETN application to hte EU Marie Curie scheme. |
Year(s) Of Engagement Activity | 2015 |
Description | Talks to school and university students |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Undergraduate students |
Results and Impact | Talks given both to undergraduate students and to student candiates on the possibility of predicting evolutionary trajectories using the approach developed during the "Towards predictive biology" BBSRC award. |
Year(s) Of Engagement Activity | 2014,2015,2016,2017,2018,2019 |
Description | Use of gene sequences and mutant libraries to identify key genes for resistance to antimicrobials |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Other audiences |
Results and Impact | As member of two regulatory committees (ACRE, for DEFRA, and SACGM for HSE) I've been involved in discussions with other committee member and directly with academics and clinicians who have submitted requests for authorisation of GM-related work (eg use of GM bacteria in clinical trial or in experiments involving members of public to track movement of commensal organisms) about the use of genome sequencing and potentially mutant libraries to identify novel resistance and susceptibility genes. |
Year(s) Of Engagement Activity | 2016,2017,2018 |