Deciphering the dual effect of lipopolysaccharides from plant pathogenic<i>Pectobacterium</i>
Bibliographic record
Abstract
Lipopolysaccharides (LPS) are a component of the outer cell surface of almost all Gram-negative bacteria and play an essential role for bacterial growth and survival. Lipopolysaccharides represent typical microbe-associated molecular pattern (MAMP) molecules and have been reported to induce defense-related responses, including the expression of defense genes and the suppression of the hypersensitive response in plants. However, depending on their origin and the challenged plant, LPS were shown to have complex and different roles. In this study we showed that LPS from plant pathogens Pectobacterium atrosepticum and Pectobacterium carotovorum subsp. carotovorum induce common and different responses in A. thaliana cells when compared to those induced by LPS from non-phytopathogens Escherichia coli and Pseudomonas aeruginosa. Among common responses to both types of LPS are the transcription of defense genes and their ability to limit of cell death induced by Pectobacterium carotovorum subsp carotovorum. However, the differential kinetics and amplitude in reactive oxygen species (ROS) generation seemed to regulate defense gene transcription and be determinant to induce programmed cell death in response to LPS from the plant pathogenic Pectobacterium. These data suggest that different signaling pathways could be activated by LPS in A. thaliana cells.
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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".