Periodic Reporting for period 1 - PathoGenoPredict (Landscape genomic prediction of plant-pathogen interactions)
Periodo di rendicontazione: 2022-03-01 al 2024-02-29
Pathogens affect every form of life, so an immune system is essential to all lifeforms. Unlike animals, plants lack an adaptive immune system that remembers past infections, instead relying on population-wide diversity at specific genes that recognise specific pathogens. These plant genes detect specific molecules from a pathogen, triggering immune responses that protect the plant from infection. Plant diseases are a major cause of both economic losses and food insecurity in agriculture, and ecological harm in the environment. Changes to plant pathogen distributions and increased virulence are second-order effects of climate change, exacerbating direct climatic impacts.
The original aim of this project was to examine the variation across the landscape in the genes underlying this immune interaction between a model plant (Arabidopsis thaliana), and its model oomycete pathogen (Hyaloperonospora arabidopsidis). The specific aims were to discover how genetic diversity at specific loci was distributed through the native range of both A. thaliana and H. arabidopsidis, and to tie genetic variation to changes in the local climate between populations, as a way to learn how this variation is guided by the environment, and later to be able to build predictive models of how this variation might change as climates change.
The original aim of this project was to examine the variation across the landscape in the genes underlying this immune interaction between a model plant (Arabidopsis thaliana), and its model oomycete pathogen (Hyaloperonospora arabidopsidis). The specific aims were to discover how genetic diversity at specific loci was distributed through the native range of both A. thaliana and H. arabidopsidis, and to tie genetic variation to changes in the local climate between populations, as a way to learn how this variation is guided by the environment, and later to be able to build predictive models of how this variation might change as climates change.
In order to learn which genes are present in which locations, first I had to collect a large number of infected plants. Unfortunately, due to both sampling limited by the COVID19 pandemic and weather conditions not conducive to infection, I was only able to obtain dozens rather than hundreds of individuals, and so refocused work to answer related questions with the collection available. To do so, I first sequenced the genome of H. arabidopsidis and annotated protein-coding genes using the latest high quality genomic methods. This represents the first complete genome sequence in the genus Hyaloperonospora, and one of only a handful of oomycete genomes with experimental evidence-based annotation of proteins. Then, we tested which combinations of over 100 different strains of both H. arabidopsidis and A. thaliana lead to infections, a total of more than 10,000 different tests. From these data, I described the broad patterns across geographic space, including that a pathogen was more likely to infect hosts from nearby where it was collected than further away, suggesting that pathogens are locally adapted to the host immune systems nearby them. I then used statistical techniques to link genetic variation at specific pathogen genes to whether or not a pathogen could infect a given set of hosts, highlighting the genes that are likely being recognised by hosts. I also developed several pieces of software required to analyse these genomic datasets, and released them as open source software free for others to use in their own research. One software package is already published as an open access peer reviewed journal article, and I am finalising another peer-reviewed journal article describing the scientific advances made throughout this project.
This project is one of very few that have attempted to study genetic interactions between plants and pathogens directly on wild pathogens. Typically, the interaction between plants and pathogens is studied using lab strains of a pathogen and of a host. While this allows a greater degree of experimental control, it is fundamentally limited by the low amount of complexity encountered in such model systems. In this project, I have combined the reproducibility of lab experiments with the breadth of genetic and functional diversity collected from across the range of a plant and its primary pathogen, enabling investigation of the genetic basis of interactions between a plant and its pathogen in unprecedented detail. By grounding collections in their geographic context, we have learned how patterns of resistance and susceptibility to plant pathogens vary over the landscape, something never before shown at this scale. More broadly, this work provides a template for work in other plant-pathogen systems, including in crops. It has also led to the publication of novel computational methods to investigate plant pathosystems, making future work in other wild pathosystems easier.