Pathogen and Pest Responses Are Altered Due to RNAi-Mediated Knockdown of <i>GLYCOALKALOID METABOLISM 4</i> in <i>Solanum tuberosum</i>
Bibliographic record
Abstract
Steroidal glycoalkaloids (SGAs) are major secondary metabolites constitutively produced in cultivated potato Solanum tuberosum, and α-solanine and α-chaconine are the most abundant SGAs. SGAs are toxic to humans at high levels but their role in plant protection against pests and pathogens is yet to be established. In this study, levels of SGAs in potato were reduced by RNA interference (RNAi)-mediated silencing of GLYCOALKALOID METABOLISM 4 (GAME4)-a gene encoding cytochrome P450, involved in an oxidation step in the conversion of cholesterol to SGA aglycones. Two GAME4 RNAi lines, T8 and T9, were used to investigate the effects of manipulation of the SGA biosynthetic pathway in potato. Growth and development of an insect pest, Colorado potato beetle (CPB), were affected in these lines. While no effect on CPB leaf consumption or weight gain was observed, early instar larval death and accelerated development of the insect was found while feeding on leaves of GAME4 RNAi lines. Modulation of SGA biosynthetic pathway in GAME4 RNAi plants was associated with a larger alteration to the metabolite profile, including increased levels of one or both the steroidal saponins or phytoecdysteroids, which could affect insect mortality as well as development time. Colonization by Verticillium dahliae on GAME4 RNAi plants was also tested. There were increased pathogen levels in the T8 GAME4 RNAi line but not in the T9. Metabolite differences between T8 and T9 were found and may have contributed to differences in V. dahliae infection. Drought responses created by osmotic stress were not affected by modulation of SGA biosynthetic pathway in potato.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".