Plant lipid metabolism in susceptible and tolerant soybean ( <i>Glycine max</i> ) cultivars in response to <i>Phytophthora sojae</i> colonization and infection
Bibliographic record
Abstract
Abstract Soybean is one of the most cultivated crops globally and a staple food for much of the world’s population. The annual global crop losses due to infection by the Phytophthora sojae are currently estimated at approximately $2B USD, yet we have limited understanding of the role of lipid metabolism in the adaptative strategies used to limit infection and crop loss. We employed a multi-modal lipidomics approach to investigate how soybean cultivars remodel their lipid metabolism to successfully limit infection by Phytophthora sojae . Both the tolerant and susceptible soybean cultivars showed alterations in lipid metabolism in response to Phytophthora sojae infection. Relative to non-inoculated controls, induced accumulation of stigmasterol was observed in the susceptible cultivar whereas, induced accumulation of phospholipids and glycerolipids occurred in tolerant soybean cultivar. We have generated a comprehensive metabolic map of susceptible and tolerant soybean root and stem lipid metabolism to identify lipid modulators of host immune or tolerance response to Phytophthora sojae infection and identified potential pathways and unique lipid biomarkers like TG(15:0/22:0/22:5), TG(10:0/10:0/10:0), TG(10:0/10:0/14:0), DG(18:3/18:3), DG(16:0/18:3) and DG(24:0/18:2) as possible targets for the development of future plant protection solutions.
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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".