Predicting the emergence of host-adapted bacterial phytopathogens

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Using low cost, high throughput genome sequencing we will ask how the population structures of Ps lineages on cultivated and wild cherry varies over time, and how much this is shaped by host genotype and local environment. Profiling absolute levels of abundance of bacterial and fungal species in the phylloplane we will ask how agronomic practice, specifically the application of nitrogen and the use of polytunnel covers affects epiphytic and pathogenic lineages in realistic field settings.

Convergent effector gain has been identified in pathogens of cherry and we will examine if effector repertoire is also adapted to colonisation of the shoot surface. We will carry out controlled evolution experiments to study whether effector rich lineages are able to colonise increasingly phylogenetically distant hosts through and whether the adaptive potential of effector poor, toxin-rich Ps lineages to shift hosts is greater than effector-rich lineages. Using phage transfer experiments under different stress-inducing conditions we ask whether there is phage-mediated effector transfer between pathogens and epiphytes as is indicated by preliminary work.

Utilising the abundance of Ps genomic data, coupled with supervised machine learning approaches, we will develop tools to predict bacterial host range from genome sequence alone. Developing a training set of genomes with well established host-pathogen association, then testing a range of feature classification techniques and machine learning method, we will evaluate predictions of host compatibility with Prunus and other plant species. Using 'deep learning' methods, we will discover further explanatory features associated with host range classification. We will validate predictions through pathogenicity tests on predicted compatible hosts.

We seek to understand how epiphytic niche, agronomic management practice and the gene exchange between bacterial lineages modify population dynamics and adaptive potential using wild and cultivated cherry. Fulfilling these objectives will allow more effective, integrated control methods to be developed.

Deploying machine-learning approaches on large datasets we also ask, whether host range can be predicted from genome sequence alone. These novel approaches will generate more powerful tools for management of bacterial diseases and the identification of invasive bacterial lineages that may pose a threat to natural species or cultivated crops.

Taken together it is likely there will be significant impacts beyond this funded research project. Indicated in brackets is the indicative timescale of the potential benefits measured from the start of the project.

Understanding movement of pathogens and potential pathogens (and pathogenicity genes) between crops and wild relatives (3-7 years)- Benefits, plant health agencies, policymakers, researchers, nurseries, growers, agroforestry schemes

This work will inform where environmental reservoirs of potentially pathogenic Pseudomonas are, how stable they are over space and time and how variable they are between crops and their wild relatives. This is all important knowledge, as coupled with other developments (see below) they can inform management practices and provide a tool to monitor the role that different crops may play in harbouring diseases that affect natural populations and vice versa. This may then inform management decisions, for example the location and composition of new agroforestry plantings on farms and the wider environment.

Development of machine learning approaches to predict host range and infection risk and precision diagnostics (4-8 years) - Benefits, plant health agencies, policymakers, researchers, nurseries, growers

The purpose of developing models to identify host range, first for research purposes but later as a tool for risk management. The movement of diseases through global trade is having significant impacts across many crops. However being able to trace the source of disease outbreaks and their origin is often challenging. Developing precision diagnostics to provide early warnings and more detailed information about whether imported plants carry potential pathogens affecting them and other close or distantly related crops would be a powerful tool in shaping both rapid responses and plant movement policy.

Development of precision control (6-12 years) (Benefits, researchers, nurseries, growers, general public)

Once the stability of bacterial populations can be assessed more rational and informed approaches to biocontrol can be undertaken, through the study of synthetic communities targeted biocontrol agents, e.g. phages, synthetic community inoculation, modified microbes etc. Further work, underpinned by this research project would then allow the decision of precise controls, potentially even adapted to the local population of microbes and disease causing agents. While the approaches would be general to many pathogenic microbes, benefits could be first realised in disease scenarios involving Ps. This could have wide ranging benefits, both for cultivated and wild plant populations.

Agronomic work (benefits 3-5 years) - Benefits growers

This work establishes orchards for long-term study and the learnings from our experiments can be taken forward to inform agronomic practice, either through larger scale trials on grower holdings funded by producer organisations or the levy body or through implementation. As managing disease effectively contributes significantly to grower profitability and total factor productivity, through reduction in losses, the impact of this research outcome will be felt through the supply chain.