Novel antibacterial peptides induced by probiotics in <scp><i>Hermetia illucens</i></scp> (Diptera: Stratiomyidae) larvae
Bibliographic record
Abstract
Abstract There is a need to discover new therapeutic substances due to the emergence of deadly infectious diseases and various antibiotic resistance. We focused on the larvae that are utilized as a medical insect for the treatment of skin damage in Europe and America. This study was to investigate the pharmacological activities of novel antibacterial peptides isolated from Hermetia illucens larvae against the Klebsiella pneumoniae and Shigella dysenteriae. The larvae were immunized by probiotics (Lactobacillus casei) for 24 h. The hemolymph from the immunized larvae was fractionated through reverse‐phase chromatography. Peptides were purified using HPLC and the coomassie blue staining, and identified using Nano‐LC‐ESI‐MS/MS system. Antibacterial activities of the peptides were evaluated by turbidometric assay, liquid broth dilution assay, resazurin assay, and agar disk diffusion method. The minimum inhibitory concentrations (MICs) of the peptides were measured as 150 μg/mL through the turbidometric, liquid broth dilution, and resazurin assays. The peptides effectively inhibited their growth/proliferation as well as the survival rate of the tested bacteria. Furthermore, the immunized larvae exhibited overexpression of the peptides compared to non‐immunized larvae. These results demonstrate that the peptides induced by H. illucens exert strong antibacterial activity against Gram‐negative bacteria. The results suggest that the activation of the humoral immunity induced by immunization functioned to enhance the production of antibacterial peptides from the insect and their antibacterial properties. This study indicates the potential of the peptides produced from larvae as antibacterial peptide substance for the development of novel antibacterial drugs.
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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".