Detection of Qol Fungicide Resistance and Genomic Analysis for Identification of Polyketide Synthases and Methyltransferases in Ascochyta rabiei
Bibliographic record
Abstract
Ascochyta blight is the most important disease of chickpeas in North America since the fungal pathogen Ascochyta rabiei was introduced to this region in 1986. Ascochyta blight epidemics can cause yield and economic losses of up to 100%. Currently available chickpea cultivars are partially resistant to A. rabiei, and the disease is primarily managed with the aid of seed and foliar fungicide treatment throughout the growing season. Development of resistance to highly effective fungicides such as the quinone outside inhibitor (Qol) fungicides has therefore significantly threatened chickpea production in the US and Canada. In this dissertation, the mutation associated with resistance to Qol fungicides was identified in A. rabiei in North Dakota from which a quick and accurate diagnostic assay was developed for the detection of Qol resistant isolates of A. rabiei. The detection of Qol resistant isolates is important for designing management strategies aimed to controlling A. rabiei in the field. Polyketide synthases (PKSs) and methyltransferases have been associated with virulence in several fungi but these genes have not been identified or characterized previously in A. rabiei. Therefore, a bioinformatic resource was developed for the identification of PKS proteins from the genome of A. rabiei. This was developed using the 13-ketoacyl synthase and acyltransferase domains from PKS proteins exclusively belonging to the fungal species and was used to identify PKS genes from the A. rabiei genome. Several methyltransferase genes were also identified using a similar strategy. The characterization of the identified A. rabiei PKSs and methyltransferases was conducted to study the effect of the divalent cations present in chickpea seed extracts on the accumulation of phytotoxic compounds, vegetative growth, and conidial production. This study provides an insight into the effect of these divalent cations on the expression of genes that modulate biological processes that may be directly associated with infection and colonization of the host.
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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) | 0.000 | 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".