Author response: Heterogeneous absorption of antimicrobial peptide LL37 in Escherichia coli cells enhances population survivability
Bibliographic record
Abstract
Many organisms use molecules called antimicrobial peptides as a first line of defense against harmful bacteria. For example, in humans, these natural antibiotics are found in the airways, on the skin and in the urinary tract. Because antimicrobial peptides are positively charged, they function in a different way compared to conventional antibiotics. They are attracted to the negatively charged surface of a bacterium, where they latch onto and penetrate through the membrane that encapsulates the microbe. While this mechanism is well studied at the molecular level, very little is known about how antimicrobial peptides spread and interact in a population of bacteria. Snoussi et al. combined several approaches to investigate the dynamics of antimicrobial peptides in Escherichia coli populations of varying densities. Experiments on single cells showed that peptides stopped the growth of bacteria, which were found to be more susceptible during the late stages of their life cycle. The dying cells then absorbed and retained a large number of antimicrobial peptides. This left fewer ‘free’ peptides that could target the other E. coli cells. In fact, when there were not enough peptides to kill all the bacteria, two sub-populations quickly emerged: one group that had stopped dividing – ‘soaking up’ the peptides – and another group that could grow unharmed. This new type of cooperation between threatened E. coli bacteria is passive, as it does not rely on any direct interactions between cells. The results by Snoussi et al. are relevant to medicine, because they highlight the relative importance for the body to produce enough new antimicrobial peptides to replenish the molecules trapped in bacteria.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.083 |
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".