Exploring the genetic basis of natural resistance to microcins
Bibliographic record
Abstract
Enterobacteriaceae produce an arsenal of antimicrobial compounds including microcins, ribosomally produced antimicrobial peptides showing diverse structures and mechanisms of action. Microcins target close relatives of the producing strain to promote its survival. Their narrow spectrum of antibacterial activity makes them a promising alternative to conventional antibiotics, as it should decrease the probability of resistance dissemination and collateral damage to the host’s microbiota. To assess the therapeutic potential of microcins, there is a need to understand the mechanisms of resistance to these molecules. In this study, we performed genomic analyses of the resistance to four microcins [microcin C, a nucleotide peptide; microcin J25, a lasso peptide; microcin B17, a linear azol(in)e-containing peptide; and microcin E492, a siderophore peptide] on a collection of 54 Enterobacteriaceae from three species: Escherichia coli, Salmonella enterica and Klebsiella pneumoniae. A gene-targeted analysis revealed that about half of the microcin-resistant strains presented mutations of genes involved in the microcin mechanism of action, especially those involved in their uptake (fhuA, fepA, cirA and ompF). A genome-wide association study did not reveal any significant correlations, yet relevant genetic elements were associated with microcin resistance. These were involved in stress responses, biofilm formation, transport systems and acquisition of immunity genes. Additionally, microcin-resistant strains exhibited several mutations within genes involved in specific metabolic pathways, especially for S. enterica and K. pneumoniae.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".