Metabolic profiling of potato cultivars varying in horizontal resistance to late blight, Phytophthora infestans
Bibliographic record
Abstract
Potato is one of the most important crops grown in Canada and all over the world. Late blight caused by P. infestans is one of the major diseases of potato and is mainly managed by fungicides application. The extensive use of fungicides not only causes adverse effects on the environment but also accelerates the development of resistance in this pathogen. Horizontal resistance is considered as the best choice to control P. infestans as it is durable over years. Breeding for durable resistance requires evaluation of hundreds of breeding lines in greenhouses and in the field. This is usually done by testing several epidemiological parameters such as infection efficiency, lesion size, latent period, and area under disease progress curve (AUDPC). These methods are time-consuming and expensive. The present study reports standardization of metabolic profiling protocols and exploration of metabolic profiling based on GC/MS as an additional tool to discriminate resistance in potato against late blight. Potato cultivars varying in horizontal resistance against late blight have been inoculated with water or the pathogen and more than 100 metabolites have been tentatively identified by GC/MS. Univariate analysis has been used to identify several pathogenesis related (PR) and defense related (DR) metabolites that have potential for application as resistance biomarker metabolites. Multivariate analysis of the abundances of metabolites (the mass spectral (MS) ion trap detector outputs were obtained using Saturn Lab Software Version 5.52 and these abundances are positively proportional to the concentration of mass ions of metabolites) in cultivars were mainly used to identify pathogenesis and resistance functions. Following pathogen inoculation, several metabolites such as amino acids, organic acids, fatty acids and sugars, were significantly increased in abundances, especially in the resistant cultivar. Other metabolites such as phenylalanine, tyrosine, shikimic acid and malonic acid detected here are well known for their direct participation in the shikimic acid, the phenylpropanoid, and the malonic acid metabolic pathways. These pathways lead to the production of several defense metabolites including antimicrobial compounds including phenolics, flavonoids and phytoalexins. The metabolic profiling technology developed here has the potential application for screening of potato breeding lines for horizontal resistance against late blight.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".