We are asking questions about how plants defend themselves against pathogens and how pathogens attempt to overcome these defences to cause disease. We use ‘omics data and computational/mathematical biology to address these questions and elucidate important aspects of the defence response. We are translating fundamental work in Arabidopsis to crops and using systems biology methodologies to enhance our understanding of plant-pathogen interactions and speed up breeding for disease resistance.
Plant disease resistance
Our lab is predominately interested in plant disease resistance: how do plants respond to pathogen infection, and how does the interaction between the plant and the pathogen determine the disease outcome? Ultimately we want to understand the interaction at a molecular level to drive strategies to enhance disease resistance of crop plants.
We have done most of our work on the interaction between the plant Arabidopsis and the fungal pathogen, Botrytis cinerea, but we are also investigating Sclerotinia sclerotiorum, a closely related pathogen, as well as bacterial pathogens Pseudomonas syringae and Ralstonia solanacearum.
Although B. cinerea is a necrotrophic pathogen with a wide-host range, we demonstrated a genetic basis for resistance of Arabidopsis to this pathogen. More recently we carried out a high-resolution time series expression profiling of the Arabidopsis response to B. cinerea infection and were able to determine the timing of gene expression changes and hence the chronology of the defence response at an unprecedented level. We used this time series data to infer network models of the transcriptional response to pathogen infection and predict genes with a critical role in disease resistance. We are currently improving the network model, determining how these critical genes are functioning and using systems biology tools to enhance our ability to predict phenotype (i.e. disease outcome) from genotype (after perturbing different genes/combinations of genes).
We are translating our knowledge and methodology from Arabidopsis to crop plants and have begun work to ask whether the topology of defence networks, and critical genes within these networks, are conserved across horticultural crops and hence applicable to multiple breeding programmes. We are also investigating whether our systems network approach is as powerful in a crop species, and its potential for understanding and improving quantitative disease resistance in non-model organisms.
Network models that are capable of accurately predicting infection outcome, and simulating the phenotypic outcome after genetic perturbations in the host will also require integration of pathogen functions and regulatory control. The ability to integrate regulatory network models of the pathogen and host, and predict points of interaction between the two organisms is a key challenge. RNA-seq enables us to capture the transcriptome of the host and pathogen simultaneously and try to address this challenge.
Plant Systems biology
We are particularly interested in the power of computational and mathematical analysis and modelling to generate novel biological insight. Elucidating the gene regulatory networks that underlie complex biological processes is challenging, even more so when trying to unravel host –pathogen interactions. Accurate network models will play a vital role in identifying key defence genes and, more importantly, predicting the combination of genes that should be altered to enhance disease resistance.
We generate and integrate large-scale genome-wide data sets, and use and design systems biology tools to analyse this data to further our understanding of plant defence. We have recently published a tool – Wigwams – that can identify statistically significant groups of genes co-expressed across subsets of expression time series. This algorithm can identify the footprint of shared regulatory mechanisms and be used to build and expand transcriptional network models.
See our 2014 review for an outline of the progress that has been made in understanding plant disease resistance by applying network modeling algorithms, and how this computational/mathematical strategy is facilitating a systems view of plant defence.
We are applying systems biology approaches to disease resistance in crop plants, targeting quantitative resistance against ubiquitous pathogens of horticultural crops that have been challenging for plant breeders. In one project, together with John Clarkson, we are investigating the interaction between lettuce and the fungal pathogen Sclerotinia sclerotiorum. By combining systems biology with more traditional quantitative genetics we aim to facilitate breeding of resistant cultivars.
Environmental Stress responses
We have been part of a large systems biology project, PRESTA, elucidating the gene regulatory networks underlying plant transcriptional responses to environmental stress – both biotic and abiotic stress. You can read more about PRESTA and access key data sets and software here.
We are integrating network models and data from biotic and abiotic stress responses to elucidate a core stress network – a network of regulatory interactions that occur in multiple stress responses – and determine key switch points within this network that provide specificity of response and/or more importantly, can be used to predict the outcome of multiple stresses occurring simultaneously. In the lab a single stress is usually applied and studied, yet in the field crops will experience multiple environmental stress conditions at once and need to adapt their responses accordingly.
Crops in the field are also subject to daily cycles of light and temperature. We are investigating how these rhythmic conditions impact disease resistance, and using the variation in disease resistance after inoculation at dawn compared to night as a tool to probe the defence regulatory networks. The topology and/or dynamics of the defence network after inoculation at dawn compared to night can identify key control points within the network.Plant Synthetic Biology
As part of the Warwick Centre for Integrative Synthetic Biology we are re-engineering plant signaling pathways and transcriptional networks to control plant responses both temporally and spatially. We are exploiting pathogen effectors to build novel synthetic tools – SynEffectors – to drive bespoke and orthogonal control of plant responses.