Antibacterial and cell penetrating effects of LFcin17–30, LFampin265–284, and LF chimera on enteroaggregative <i>Escherichia coli</i>
Bibliographic record
Abstract
Lactoferrin (LF) is a protein with antimicrobial activity, which is conferred in part by 2 regions contained in its N-terminal lobe. These regions have been used to develop the following synthetic peptides: lactoferricin17-30, lactoferrampin265-284, and LF chimera (a fusion of lactoferricin17-30 and lactoferrampin265-284). We have reported that these LF peptides have antibacterial activity against several pathogenic bacteria; however, the exact mechanism of action has not been established. Here, we report the effects of LF peptides on the viability of enteroaggregative Escherichia coli (EAEC) and the ability of these peptides to penetrate into the bacteria cytoplasm. The viability of EAEC treated with LF peptides was determined via enumeration of colony-forming units, and the binding and internalization of the LF peptides was followed via immunogold labeling and electron microscopy. Treatment of EAEC with 20 and 40 μmol/L LF peptides reduced bacterial growth compared with untreated bacteria. Initially the peptides associated with the plasma membrane, but after 5 to 30 min of incubation, the peptides were found in the cytoplasm. Remarkably, bacteria treated with LF chimera developed cytosolic electron-dense structures that contained the antimicrobial peptide. Our results suggest that the antibacterial mechanism of LF peptides on EAEC involves their interaction with and penetration into the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".