Central metabolism is a key player in <i>E. coli</i> biofilm stimulation by sub-MIC antibiotics
Bibliographic record
Abstract
Abstract Exposure of Escherichia coli to sub-inhibitory antibiotics stimulates biofilm formation through poorly characterized mechanisms. Using a high-throughput Congo Red binding assay to report on biofilm matrix production, we screened ∼4000 E. coli K12 deletion mutants for deficiencies in this biofilm stimulation response. Mutants lacking acnA, nuoE , or lpdA failed to respond to sub-MIC novobiocin, implicating central metabolism and aerobic respiration in biofilm stimulation. These genes are members of the ArcA/B regulon – controlled by a respiration-sensitive two-component system. Mutants of arcA and arcB had a ‘pre-activated’ phenotype, where biofilm formation was already high relative to wild type in vehicle control conditions and failed to increase further with the addition of sub-MIC antibiotics. Supporting a role for respiratory stress, the biofilm stimulation response was inhibited when nitrate was provided as an alternative electron acceptor. Deletion of genes encoding the nitrate respiratory machinery abolished its effects, and nitrate respiration increased during growth with sub-MIC antibiotics. In probing the generalizability of biofilm stimulation, we found that the stimulation response to translation inhibitors was minimally affected by nitrate supplementation. Finally, using a metabolism-sensitive dye, we showed spatial co-localization of increased respiration with sub-MIC bactericidal antibiotics. By characterizing the biofilm stimulation response to sub-MIC antibiotics at a systems level, we identified multiple avenues for design of therapeutics that impair bacterial stress management.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".