Understanding the structure and variation within the genome of the pathogenic ascomycete fungus Leptosphaeria maculans

Abstract

The ascomycete fungus Leptosphaeria maculans is a major pathogen of Brassica species, particularly canola (Brassica napus; rapeseed; oil-seed rape), and the primary cause of crop losses of canola in Australia, causing blackleg disease. L. maculans, a filamentous ascomycete, is the causal agent of phoma stem canker, commonly referred to as blackleg.In late stages of infection, it spreads through the stem vasculature causing lesions, leading to poor growth, lodging and eventually plant death. This fungus is found in canola-growing regions worldwide such as Australia, Canada and Europe. Increased production of canola in these regions has led to a rise in the severity of the disease. In Australia alone, L. maculans infection is responsible for an estimated Australian $100 million in crop losses each year, with average losses ranging from 15-48 % and significant efforts are underway to improve resistance to this disease.Understanding the characteristics of L. maculans is vital for developing an effective and sustainable approach to the management of blackleg disease on Brassica species. The completion of the L. maculans genome sequence was a significant development in the study of this fungal pathogen and provides a reference genome to which molecular markers can be physically mapped. This has been highly useful in other plant pathogens with sequenced genomes, such as the wheat pathogen Parastagonospora nodorum and the cereal pathogen Fusarium graminearum. Importantly, a reference genome also allows mapping of whole genome re-sequencing data, which is becoming a high-throughput, costeffective method to study genome-wide diversity, particularly for the relatively small, lower complexity genomes of many fungal species. By re-sequencing the genome of different L. maculans isolates, variations in genome sequence and structure can be elucidated.Advances in genome sequencing technologies have revolutionised plant and fungal genomics. They have made genome sequencing, re-sequencing and Single Nucleotide Polymorphism (SNP) discovery highly accessible, high-throughput and cost-effective. The process of whole genome re-sequencing involves aligning millions of short sequence reads to a reference genome sequence. Once this has been achieved, it is possible to identify genetic variation between individuals, which can be linked to variation in phenotype to provide molecular genetic markers and insights into gene function.Sequence variation can have a major impact on how an organism develops and responds to the environment. III | P a g eThis thesis describes the implementation of several approaches to elucidating the genome structure and variation of a number of L. maculans isolates, including SNPs and presence/absence variations (PAVs).Initially, the re-sequencing of two L. maculans isolates for the identification of 21,814 SNPs was performed. I demonstrated the application of a novel SNP calling method, SGSautoSNP and its robustness and sensitivity in identifying polymorphisms in L.maculans. I described the use of these S...

Population diversity and genomics of the fungal pathogen of canola Leptosphaeria maculans

“…PAVs were also purported to be the reason behind M. oryzae evading the host immune response (Huang et al, 2014). Zander (2015) details the detection of PAVs in the L. maculans genome which highlighted selection pressure in some regions of the genome and successfully validated the presence/absence of avirulence genes like AvrLm1 and AvrLm6.…”

“…PAKAP or Presence/Absence variation (PAV) prediction identifies unique reads present only in one sample compared to others and does not require a reference genome (Zander, 2015). This method helps identify those effectors that are not present in the reference genome without the need to sequence and assemble whole genomes.…”

“…Next, it highlights K-mers that are unique to each sample and finally pulls out whole sequence reads corresponding to each unique K-mer. Details of the working of this method can be obtained from Zander (2015).…”

“…Initial analyses of these SNPs indicate them to be RIP mutations. Of the SNPs detailed in Table 5.2, SNP73 from previous predictions was not reported by the predictions in Zander (2015) resource due to the difference in the isolates based on which SNP prediction was conducted. Chapter 3 (Zander et al, 2013) used isolates 04MGPS021 and 04MGPP041 for SNP prediction whereas the SNP resource used here (Zander, 2015) used isolates D1, D2, D3, D4, D5, D6, D8, D9, D10 and D12.…”

“…The availability of large genomic data-sets has led to the development of several bioinformatics tools that assist the user in understanding the biology behind sequencing details. SGSGeneLoss (Golicz et al, 2015), SGSautoSNP (Lorenc et al, 2012) and Present/Absent K-mer Analysis Pipeline (PAKAP) (Zander, 2015) prediction are three different bioinformatics tools that highlight various genomic features of a sample in three different manners.…”