*NERC E3* *NERC E3* Genome duplication in parasitic plants: features and consequences

Interested individuals must follow the "how to apply" link on the Geosciences E3 Doctoral Training Partnership web page: http://e3dtp.geos.ed.ac.uk/apply.html

Parasitism is a ubiquitous feature of the natural world, found in every major organismal group, and across every ecosystem. In plants, parasitism is an extremely successful strategy that has evolved independently on at least a dozen occasions. There are over 4000 parasitic species. These parasitic plants include crop pests (Orobanche, Striga) and ecosystem engineers that affect community assembly (Rhinanthus and many hemiparasites). Studies of parasitic plants are essential for improving food security and in understanding ecosystem services, while also providing important insights into the origin of novel evolutionary strategies.
A major goal in parasitic plant biology is to understand the genetic basis of the parasitic habit. Comparative transciptomic analyses identified ~180 genes upregulated in the haustorium (the parasite attachment organ) shared between three divergent taxa (Yang et al., 2015). Many of these genes are the products of gene duplication that has occurred since divergence from non-parasitic relatives. This raises the question of whether genome duplication plays an important role in the origin of plant parasitism.
This project will use genomic tools to investigate the presence and evolutionary fate of duplicate gene copies in parasitic eyebrights (Euphrasia). These generalist parasitic plant species are extremely ecologically diverse, and include a number of recently evolved UK endemics of conservation concern. They are particularly well-suited to genomic analysis because they have small genomes compared to other parasitic plants. The research project will involve:
• assembling the nuclear genome of a diploid Euphrasia species from next generation and single-molecule data
• assembling the genome of a tetraploid Euphrasia
• performing gene finding and rich functional annotation using RNA-seq data
• comparing diploid and tetraploid Euphrasia (and related non-parasitic taxa) to identify retained duplicates and explore their functions and evolutionary histories
• using low-coverage and resequencing data from all 19 British native taxa and new lines collected from European populations that may represent independent genome duplication events to explore pattern and process of duplicate evolution across the genus.
These new insights will be essential for understanding the evolution of plant parasitism, and may represent a step towards developing genetic tools for control of devastating crop parasites.

Key research questions
What are the genomic changes that have occurred between parasitic Euphrasia and related
non-parasitic lineages?
Which duplicated gene copies have been retained in the genome of tetraploid Euphrasia?
Do comparative evolutionary analyses of parasitic plants point towards a common set of genes
involved in plant parasitism?


Methodology
1. Generating single-molecule, long read data for assembly of Euphrasia genomes
2. Deploying high performance computing toolkits (virtual "cloud computing") to perform
assembly and annotation of diploid and tetraploid Euphrasia
3. Building a rich interactive database of Euphrasia and comparator genomes to present
assembly and annotation data
4. Comparing the gene content and structure of Euphrasia and the related non-parasitic
Mimulus guttatus
5. Field collection and sequencing of European Euphrasia, and analysis of genomic diversity
6. Broad-scale comparative analysis of parasitic and non-parasitic genomes

Training
A comprehensive training programme will be provided comprising both specialist scientific
training and generic transferable and professional skills. Training will include: (a)
bioinformatics in python and statistical analysis in R, (b) specialist training in genome
assembly and annotation, including advanced cloud computing, (c) field sampling, plant
identification, DNA extraction and next generation sequencing.

Requirements
The applicant should have an interest in bioinformatics and biodiversity, and a willingness to
learn comparative genomic analysis. Prior experience in bioinformatics and statistical analysis
in R would be beneficial but not essential.