Exposure to Sub-lethal 2,4-Dichlorophenoxyacetic Acid Arrests Cell Division and Alters Cell Surface Properties in Escherichia coli
Bibliographic record
Abstract
Escherichia coli is a robust, easily adaptable and culturable bacterium in vitro, and a model bacterium for studying the impact of xenobiotics in the environment. We have used correlative atomic force - laser scanning confocal microscopy (AFM-LSCM) to characterize the mechanisms of cellular response to the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). One of the most extensively used herbicides world-wide, 2,4-D is known to cause hazardous effects in diverse non-target organisms. Sub-lethal concentrations of 2,4-D caused DNA damage in E. coli WM1074 during short exposure periods which increased significantly over time. In response to 2,4-D, FtsZ and FtsA relocalized within seconds, coinciding with the complete inhibition of cell septation and cell elongation. Exposure to 2,4-D also resulted in increased activation of the SOS response. Changes to cell division were accompanied by concomitant changes to surface roughness, elasticity and adhesion in a time-dependent manner. This is the first study describing the mechanistic details of 2,4-D at sub-lethal levels in bacteria. Our study suggests that 2,4-D arrests E. coli cell division within seconds after exposure by disrupting the divisome complex, facilitated by dissipation of membrane potential. Over longer exposures, 2,4-D causes filamentation as a result of an SOS response to oxidative stress induced DNA damage.
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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".