Rhizobium leguminosarum bv. viciae 3841 Adapts to 2,4-Dichlorophenoxyacetic Acid with “Auxin-Like” Morphological Changes, Cell Envelope Remodeling and Upregulation of Central Metabolic Pathways
Bibliographic record
Abstract
There is a growing need to characterize the effects of environmental stressors at the molecular level on model organisms with the ever increasing number and variety of anthropogenic chemical pollutants. The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), as one of the most widely applied pesticides in the world, is one such example. This herbicide is known to have non-targeted undesirable effects on humans, animals and soil microbes, but specific molecular targets at sublethal levels are unknown. In this study, we have used Rhizobium leguminosarum bv. viciae 3841 (Rlv) as a nitrogen fixing, beneficial model soil organism to characterize the effects of 2,4-D. Using metabolomics and advanced microscopy we determined specific target pathways in the Rlv metabolic network and consequent changes to its phenotype, surface ultrastructure, and physical properties during sublethal 2,4-D exposure. Auxin and 2,4-D, its structural analogue, showed common morphological changes in vitro which were similar to bacteroids isolated from plant nodules, implying that these changes are related to bacteroid differentiation required for nitrogen fixation. Rlv showed remarkable adaptation capabilities in response to the herbicide, with changes to integral pathways of cellular metabolism and the potential to assimilate 2,4-D with consequent changes to its physical and structural properties. This study identifies biomarkers of 2,4-D in Rlv and offers valuable insights into the mode-of-action of 2,4-D in soil bacteria.
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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) | 0.000 | 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 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".