Differences in Antibiotic-Induced Oxidative Stress Responses between Laboratory and Clinical Isolates of Streptococcus pneumoniae
Bibliographic record
Abstract
Oxidants were shown to contribute to the lethality of bactericidal antibiotics in different bacterial species, including the laboratory strain Streptococcus pneumoniae R6. Resistance to penicillin among S. pneumoniae R6 mutants was further shown to protect against the induction of oxidants upon exposure to unrelated bactericidal compounds. In the work described here, we expanded on these results by studying the accumulation of reactive oxygen species in the context of antibiotic sensitivity and resistance by including S. pneumoniae clinical isolates. In S. pneumoniae R6, penicillin, ciprofloxacin, and kanamycin but not the bacteriostatic linezolid, erythromycin, or tetracycline induced the accumulation of reactive oxygen species. For the three bactericidal compounds, resistance to a single molecule prevented the accumulation of oxidants upon exposure to unrelated bactericidal antibiotics, and this was accompanied by a reduced lethality. This phenomenon does not involve target site mutations but most likely implicates additional mutations occurring early during the selection of resistance to increase survival while more efficient resistance mechanisms are being selected or acquired. Bactericidal antibiotics also induced oxidants in sensitive S. pneumoniae clinical isolates. The importance of oxidants in the lethality of bactericidal antibiotics was less clear than for S. pneumoniae R6, however, since ciprofloxacin induced oxidants even in ciprofloxacin-resistant S. pneumoniae clinical isolates. Our results provide a clear example of the complex nature of the mode of action of antibiotics. The adaptive approach to oxidative stress of S. pneumoniae is peculiar, and a better understanding of the mechanism implicated in response to oxidative injury should also help clarify the role of oxidants induced by antibiotics.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".