A strategy to re-sensitise drug-resistant Gram-positive bacteria to oxazolidinone-class antibiotics
Bibliographic record
Abstract
BACKGROUND: Multidrug-resistant bacterial infections have high mortality rates and few treatment options. Synergistic combinations may improve clinical outcome but traditional strategies often damage healthy microbiome. Oxazolidinone-class antibiotics are typical last-resort drugs for treating drug-resistant bacterial infections but are becoming less effective due to resistance development. METHODS: After high-throughput screening, synergy was further assessed by in vitro indices (like fractional inhibitory concentration index, biofilm formation and resistance development) and in vivo symptoms in animals with skin and ocular bacterial infections (and ocular microbiome extraction analysis). Proteomics, chemical synthesis, multi-microscopy techniques and antibiotic real-time/kinetic accumulation were employed to explore mechanisms and expand translational applications. FINDINGS: Combining phosphorylated oxazolidinone-class antibiotics with positively charged compounds (lysozyme as native representative) resulted in broad-spectrum drug re-sensitisation. In representative combination, urea cycle was disrupted to alkalinise cytoplasm, which subsequently activated alkaline phosphatase to promote conversion of phosphorylated prodrug to active form. By introducing concept of restored healthy microbiome as the evaluated index in antibiotic therapy, we confirmed excellent translational and microbiome-friendly potential of this strategy in clinical settings because it not only inhibited biofilm formation and development of drug-resistant mutations in vitro, but also alleviated symptoms in infected animals including the restoration of healthy microbiome. INTERPRETATION: As both agents have excellent safety profiles, such clinical investigation may immediately be contemplated in humans. Translationally, scientists benefit from strategy by simultaneously achieving greater efficacy (>500-fold re-sensitisation) and higher safety (prodrug-based and microbiome-friendly strategy especially when active form may be toxic). FUNDING: Collaborative Research Funds from Research Grants Council (C5033-19E).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".