The effects of sub-lethal antibiotics on bacterial physiology
Bibliographic record
Abstract
Antibiotics are small molecules that kill bacteria by inhibiting essential processes. However, the concentrations used to kill bacteria in a clinical setting are typically much higher than the concentrations generated in nature, where most antibiotics are secreted by microbes. This discrepancy in concentrations, combined with a recognition that the human use of antibiotics bears little resemblance to the role of antibiotics in nature, prompted questions about whether growth inhibition was the primary function of antibiotics. Studying the effects of antibiotics at sub-lethal concentrations on bacteria could provide new insights into the natural role of antibiotics. One striking effect of bacterial encounters with sub-lethal antibiotics is the stimulation of biofilm formation. Biofilms are surface-adhered communities of bacteria. The biofilm lifestyle confers many benefits for bacteria and is a major mode of bacterial growth. Therefore, the ability of sub-lethal antibiotics to cause a transition from planktonic to biofilm growth indicates that antibiotics could be a driving force behind the assembly and abundance of bacterial communities in nature. Chapters Two and Three investigate the underlying mechanisms of this response in Escherichia coli and Pseudomonas aeruginosa, and suggest that sub-lethal antibiotics perturb central metabolism and respiration, changes that are sensed and relayed into increased biofilm formation to provide population-level protection. Chapters Four and Five investigate the effects of sub-lethal antibiotics on peptidoglycan metabolism in P. aeruginosa and E. coli. Peptidoglycan is an essential macromolecule for bacterial survival and is deeply integrated into their physiology. Furthermore, peptidoglycan synthesis is among the most favoured targets of antibiotics. Chapter Four investigates interactions between peptidoglycan-targeting antibiotics and folate metabolism-targeting antibiotics, and characterizes an overlooked connection between folate and peptidoglycan metabolism. Based on this work, we rationally designed a new inhibitor that potentiates folate and peptidoglycan-targeting antibiotics. Chapter Five sheds new light on peptidoglycan recycling by leveraging a pathway in P. aeruginosa for sensing and responding to sub-lethal doses of PG-targeting antibiotics. Finally, Chapter Six summarizes the understanding gained from Chapters Two through Five and synthesizes this information for broader insights on the possible roles of antibiotics in nature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".