Visualizing bacterial quorum sensing and quorum quenching interaction on Medicago roots
Bibliographic record
Abstract
Abstract Background Defining interactions of bacteria in the rhizosphere (area near and on the plant root) is important to understand how they affect plant health. Some rhizosphere bacteria, including plant growth promoting rhizobacteria (PGPR) engage in the intraspecies communication known as quorum sensing (QS). Many use the extracellular autoinducer signal molecule N-acyl homoserine lactone (AHL) and other rhizobacteria, including PGPR, can interfere or disrupt QS through quorum quenching (QQ). Traditional AHL biosensor assays used for screening and identifying QS and QQ bacteria interactions fail to account for the role of the plant root. Methods Medicago spp. seedlings germinated on Lullien’s agar are transferred to soft-agar plates containing the broad-range AHL biosensor Agrobacterium tumefaciens KYC55 and X-gal substrate. Cultures of QS and QQ bacteria as well as pure AHLs and QQ protein are applied to the plant roots and incubated for 3 days. Results We show that this expanded use of an AHL biosensor successfully allowed for visualization of QS/QQ interactions localized at the plant root. KYC55 detected pure AHLs as well as AHLs from live bacteria cultures grown directly on the media. We also showed clear detection of QQ interactions occurring in the presence of the plant root. Conclusions Our novel tri-trophic system using an AHL biosensor is useful to study QS interspecies interactions in the rhizosphere.
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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 | 0.000 |
| 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".