Peptidoglycan Hydrolase Fusions Maintain Their Parental Specificities
Bibliographic record
Abstract
The increased incidence of bacterial antibiotic resistance has led to a renewed search for novel antimicrobials. Avoiding the use of broad-range antimicrobials through the use of specific peptidoglycan hydrolases (endolysins) might reduce the incidence of antibiotic-resistant pathogens worldwide. Staphylococcus aureus and Streptococcus agalactiae are human pathogens and also cause mastitis in dairy cattle. The ultimate goal of this work is to create transgenic cattle that are resistant to mastitis through the expression of an antimicrobial protein(s) in their milk. Toward this end, two novel antimicrobials were produced. The (i) full-length and (ii) 182-amino-acid, C-terminally truncated S. agalactiae bacteriophage B30 endolysins were fused to the mature lysostaphin protein of Staphylococcus simulans. Both fusions display lytic specificity for streptococcal pathogens and S. aureus. The full lytic ability of the truncated B30 protein also suggests that the SH3b domain at the C terminus is dispensable. The fusions are active in a milk-like environment. They are also active against some lactic acid bacteria used to make cheese and yogurt, but their lytic activity is destroyed by pasteurization (63 degrees C for 30 min). Immunohistochemical studies indicated that the fusion proteins can be expressed in cultured mammalian cells with no obvious deleterious effects on the cells, making it a strong candidate for use in future transgenic mice and cattle. Since the fusion peptidoglycan hydrolase also kills multiple human pathogens, it also may prove useful as a highly selective, multipathogen-targeting antimicrobial agent that could potentially reduce the use of broad-range antibiotics in fighting clinical infections.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".