A prophylactic probiotic to fight paenibacillus larvae infection in honey bees
Bibliographic record
Abstract
Honey bees are the most critical crop pollinators worldwide and for the last decades mostly a single antibiotic has been used to protect them from American foulbrood (AFB), a lethal larval disease caused by the bacterium Paenibacillus larvae. Oxytetracycline (OTC) is the only approved prophylactic antibiotic for P. larvae infection in Canada, which has triggered resistant strains. While only the first larvae stages are vulnerable to AFB, consequences are lethal. Early microbiology, genomics and proteomics studies have identified molecular differences between the AFB resistant honey bee stages and the vulnerable larvae. Profiles of the immune factors, such as antimicrobial peptides (AMPs), and a characteristic commensal gut bacteria (CGB) community are key differences. Through this work we want to develop a new prophylactic for AFB, based on the molecular differences between the adult honey bee and the larvae. Specifically, a commensal bacterium strain that expresses and secretes naturally occurring AMPs targeting P. larvae vegetative cells. To identify AMPs candidates to be used as potential prophylactics, we pursued to uncover host-microbe specific responses of the adult honey bee. Through susceptibility assays we tested the inhibitory activity of characteristic honey bee and fruit fly naturally occurring AMPs against P. larvae, CGB (Bartonella apis, S. alvi and Bifidobacterium asteroides), and non-commensal bacteria (Bacillus subtills and Escherichia coli). To look for potential dysbiosis caused on the gut microbiota caused by the candidate AMPs we conducted an in vivo feeding experiment and proceeded with quantitative PCR (qPCR) and 16S rRNA deep amplicon sequencing, determining changes in size and composition. Jelleine and melittin (AMPs) were chosen as final candidates to attempt to be expressed and secreted by S. alvi. Then, we predicted the Sec pathway-dependent extracellular proteins and with a mass spectrometry based-proteomics experiment explored the signal peptides secretion dynamics. Finally, we suggested the signal peptides to be used for the expression and secretion of jelleine and melittin in S. alvi., offering a pioneering approach in the understanding and application of honey bee gut commensals.
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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 | 0.000 |
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".