Biogenic ammonia modifies antibiotic resistance at a distance in physically separated bacteria
Bibliographic record
Abstract
Bacteria release low-molecular-weight by-products called secondary metabolites, which contribute to bacterial ecology and biology. Whereas volatile compounds constitute a large class of potential infochemicals, their role in bacteria-bacteria interactions remains vastly unexplored. Here we report that exposure to gaseous ammonia released from stationary-phase bacterial cultures modifies the antibiotic resistance spectrum of all tested Gram-negative and Gram-positive bacteria. Using Escherichia coli K12 as a model organism, and increased resistance to tetracycline as the phenotypic read-out, we demonstrate that exposure to ammonia generated by the catabolism of l-aspartate increases the level of intracellular polyamines, in turn leading to modifications in membrane permeability to different antibiotics as well as increased resistance to oxidative stress. We show that the inability to import ammonia via the Amt gas channel or to synthesize polyamines prevent modification in the resistance profile of aerially exposed bacteria. We therefore provide here the first detailed molecular characterization of widespread, long-range chemical interference between physically separated bacteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".