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Assessment and engineering of the Brassica self-incompatibility locus to enhance seed production

Principal Supervisor: Dr Graham Teakle

Secondary Supervisor(s): Dr Guy Barker

University of Registration: University of Warwick

BBSRC Research Themes:

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Deadline: 4 January, 2024


Project Outline

Genetic diversity enables species to adapt to changing environments. If a plant produces pollen that fertilises its own stigma, a process known as self-pollination, the resulting progeny will lose genetic diversity and could become inbred and thus less competitive. Many plant species have developed a self-incompatibility (SI) system to reduce the chances of this happening and hence promote genetic diversity through favouring genetic exchange through outcrossing. Brassica oleracea is an important crop species that includes a number of vegetables, such as cabbage, cauliflower, broccoli, kohl-rabi, Brussels sprout and kales, and is known to possesses such an SI system. The Brassica SI system is known to be primarily determined by two genes located adjacent to each other at a single genetic locus (the S-locus). One gene encodes a pollen coat protein, known as the cysteine-rich protein (SCR), while the other encodes the S-locus receptor kinase (SRK) that is expressed on the stigma surface. These two genes have co-evolved as matched pairs at a single S-locus allele, where the SRK protein recognises the matching SLG in the pollen. When this happens a signalling mechanism is initiated that prevents the pollen from germinating on the stigma and so preventing pollination. Approximately sixty different S-alleles have been identified in B. oleracea. When a pollen from one S-allele lands on a stigma with a different S-allele there is no self-recognition and pollen germination takes place, leading to fertilisation and seed production.

While the Brassica SI system is important for maintaining diversity in wild plants, it can be a hindrance to breeders and researchers who wish to make pure-bred lines (such as varieties, or the parents of F1 hybrid varieties), due to the challenge it presents for generating sufficient quantities of seed. There is therefore a science and industry-wide interest in being able to overcome this Brassica SI system to speed up the breeding process. Developing a technique by which the process could be controlled would lead to significant economic benefits.

There are still many aspects about how the SI system works that are not well understood, and most of the individual S-loci have not been characterised at the sequence level. At Warwick we possess a diverse collection of several hundred B. oleracea genotypes (BolDFFS) that is a valuable resource in which to search for new traits relevant to sustainable agriculture. The project will be initiated by sequencing the S-locus genotype from these BolDFFS lines and to use public databases to search for other alleles. With the recently developed CRISPR-Cas9 genome editing technology, there is now the potential to genetically knock-out genes associated with SI. However, it is not practical to do this for each of the sixty different S-alleles. Instead, it would make sense to target a gene in the signal transduction pathway that presumable works with any of the S alleles. The project aims to investigate the pathway and to attempt to identify an appropriate signal transduction genes which could be knockout or regulated in order to be able to control the SI process.

References

Wang et al. (2023) Genetic Components of Self-Incompatibility in Brassica Vegetables. Horticulturae, 9, 265. https://doi.org/10.3390/horticulturae9020265

Durand et al. (2020) Evolution of self-incompatibility in the Brassicaceae: Lessons from a textbook example of natural selection. Evolutionary Applications 13:1279–1297. DOI: 10.1111/eva.12933

Techniques

    • Plant raising, pollination and seed production.
    • Microscopic analysis of pollen-stigma interaction (light microscopy, confocal microscopy, possibly scanning electron microscopy).
    • DNA isolation and comparative S-locus gene sequence analysis in the BolDFFS, together with sequence analysis of signal transduction proteins
    • Bioinformatics analysis of sequence data, including phylogenetic analysis
    • S-locus gene and signal transduction gene expression analysis
    • CRISPR-Cas9 genome editing
    • Demonstration of the effectiveness of the KO SI for improved seed production