Co-regulation of biofilm formation and antimicrobial resistance in Acinetobacter baumannii: from mechanisms to therapeutic strategies
Bibliographic record
Abstract
In recent years, multidrug-resistant Acinetobacter baumannii has emerged globally as a major threat to the healthcare system. It is now listed by the World Health Organization as a priority one for the need of new therapeutic agents. A. baumannii has the capacity to develop robust biofilms on biotic and abiotic surfaces. Biofilm development allows these bacteria to resist various environmental stressors, including antibiotics and lack of nutrients or water, which in turn allows the persistence of A. baumannii in the hospital environment and further outbreaks. Investigation into therapeutic alternatives that will act on both biofilm formation and antimicrobial resistance (AMR) is sorely needed. The aim of the present review is to critically discuss the various mechanisms by which AMR and biofilm formation may be co-regulated in A. baumannii in an attempt to shed light on paths towards novel therapeutic opportunities. After discussing the clinical importance of A. baumannii, this critical review highlights biofilm-formation genes that may be associated with the co-regulation of AMR. Particularly worthy of consideration are genes regulating the quorum sensing system AbaI/AbaR, AbOmpA (OmpA protein), Bap (biofilm-associated protein), the two-component regulatory system BfmRS, the PER-1 β-lactamase, EpsA, and PTK. Finally, this review discusses ongoing experimental therapeutic strategies to fight A. baumannii infections, namely vaccine development, quorum sensing interference, nanoparticles, metal ions, natural products, antimicrobial peptides, and phage therapy. A better understanding of the mechanisms that co-regulate biofilm formation and AMR will help identify new therapeutic targets, as combined approaches may confer synergistic benefits for effective and safer treatments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".