Population Genomics of Cercospora beticola
Bibliographic record
Abstract
Fungal plant pathogens pose a serious threat to global food safety and security, and can result in significant yield loss. Fungal plant pathogens have evolved with their hosts during the history of crop domestication. While some fungal phytopathogens of modern crops have maintained the ability to infect the wild relatives of these crops, many have evolved host-specificity due to the evolutionary arms race. Co-evolution between plants and their pathogens spans many generations. Therefore, we have employed the pathosystem of the fungal pathogen Cercospora beticola and its hosts Beta vulgaris ssp. (domesticated beet) and B. maritima (sea beet). This pathosystem is exceptional as sugar beet has a relatively short domestication history of ~300 years, compared to several thousand year history of other modern crops. Investigating the effect crop domestication has on fungal evolution in such a short time frame may provide insight into the early processes underlying the evolution of host-specificity. The availability of whole genome sequencing data for entire populations of fungal plant pathogens has enabled detailed analyses of genomic variation within and among field populations. Using population genomic data, we are able to detect population structure of a phytopathogenic fungus, identify regions that are highly differentiated between isolates, and predict the evolutionary trajectory of disease epidemics. The primary focus of this thesis was to describe the population genomics of the fungus Cercospora beticola, and determine the influence of host domestication on recent evolution and population structure of the fungus. Chapter 1 addressed the challenge of assembling and analysing population genomic data of species with structural variation, as is the case for many pathogenic fungi. We compared and contrasted two variant calling methods used in population genomics. We show that the commonly used method of variant calling, reference mapping-based approaches, as well as more recently adapted multiple genome alignment-based methods perform equally well at high sequencing depths in species with variable amounts of repetitive content. However, we also found that reference mapping-based approaches are reliable at average and high sequencing depths, regardless of repetitive content. In Chapter 2, we analyse the population genetic structure of C. beticola with the aim of comparing the genetic variation in populations of domesticated and wild beet species. Specifically, we make use of population genomics tools to elucidate whether C. beticola isolates from wild and domesticated hosts show strong signals of host specialisation. Sugar beet is comparatively novel crop, and provides insight into the early specialisation processes pathogens of domesticated plants. We collected isolates from wild and domesticated beet from Europe and North America and show that there are not clear populations of C. beticola isolates that infect wild or domesticated beet. We show that C. beticola isolates are likely a global population, with substantial admixture between individuals from all hosts and locations. While there is admixture between individuals from all locations, isolates from sea beet in the UK showed more differentiation from the isolates from other locations suggesting some barriers to gene flow and distinct population histories of the sea beet isolates. We investigated regions where the isolates from the UK are different from isolates from mainland Europe and North America, and showed that there are likely phenotypic differences between isolates from Croatian sea beet and the English sea beet isolates. We illustrate a region where the isolates from Croatia contained a premature stop codon in a gene involved in the production of an aflatoxin in high frequency, while it was present at a low frequency the isolates from English sea beet. Thus, we show that while C. beticola may not show strong signatures of host specialisation yet, there are some differences between isolates from different locations indicating the potential for future population divergence. In Chapter 3, we compare and contrast C. beticola to four other Cercospora species to elucidate differences and similarities in genome content and synteny within the genus. We show that C. beticola has a higher number of genes encoding proteins that are involved host-pathogen interaction. We also note that the other Cercospora species that has a broad host range included in this, C. cf. flagellaris, has a similar repertoire of genes. We also show that these two species share substantial synteny. We postulate that they most recent common ancestor of these two species likely had a plastic genome that underwent several translocation events. Taken together, we show that the Cercospora genus is shaped by its interactions with its environment and the various hosts. We show that C. beticola has not yet shown strong association with either host or location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.489 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".