Bacteriocins to Thwart Bacterial Resistance in Gram Negative Bacteria
Bibliographic record
Abstract
An overuse of antibiotics both in human and animal health and as growth promoters in farming practices has increased the prevalence of antibiotic resistance in bacteria. Antibiotic resistant and multi-resistant bacteria are now considered a major and increasing threat by national health agencies, making the need for novel strategies to fight bugs and super bugs a first priority. In particular, Gram-negative bacteria are responsible for a high proportion of nosocomial infections attributable for a large part to Enterobacteriaceae, such as pathogenic Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. To cope with their highly competitive environments, bacteria have evolved various adaptive strategies, among which the production of narrow spectrum antimicrobial peptides called bacteriocins and specifically microcins in Gram-negative bacteria. They are produced as precursor peptides that further undergo proteolytic cleavage and in many cases more or less complex posttranslational modifications, which contribute to improve their stability and efficiency. Many have a high stability in the gastrointestinal tract where they can target a single pathogen whilst only slightly perturbing the gut microbiota. Several microcins and antibiotics can bind to similar bacterial receptors and use similar pathways to cross the double-membrane of Gram-negative bacteria and reach their intracellular targets, which they also can share. Consequently, bacteria may use common mechanisms of resistance against microcins and antibiotics. This review describes both unmodified and modified microcins (lasso peptides, siderophore peptides, nucleotide peptides, linear azole(in)e-containing peptides), highlighting their potential as weapons to thwart bacterial resistance in Gram-negative pathogens and discusses the possibility of cross-resistance and co-resistance occurrence between antibiotics and microcins in Gram-negative bacteria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".