Delivery mechanism can enhance probiotic activity against honey bee pathogens
Bibliographic record
Abstract
Managed honey bee (Apis mellifera) populations play a crucial role in supporting pollination of food crops but are facing unsustainable colony losses, largely due to rampant disease spread within agricultural environments. While mounting evidence suggests that select lactobacilli strains (some being natural symbionts of honey bees) can protect against multiple infections, there has been limited validation at the field-level and few methods exist for applying viable microorganisms to the hive. Here, we compare how two different delivery systems-standard pollen patty infusion and a novel spray-based formulation-affect supplementation of a three-strain lactobacilli consortium (LX3). Hives in a pathogen-dense region of California are supplemented for 4 weeks and then monitored over a 20-week period for health outcomes. Results show both delivery methods facilitate viable uptake of LX3 in adult bees, although the strains do not colonize long-term. Despite this, LX3 treatments induce transcriptional immune responses leading to sustained decreases in many opportunistic bacterial and fungal pathogens, as well as selective enrichment of core symbionts including Bombilactobacillus, Bifidobacterium, Lactobacillus, and Bartonella spp. These changes are ultimately associated with greater brood production and colony growth relative to vehicle controls, and with no apparent trade-offs in ectoparasitic Varroa mite burdens. Furthermore, spray-LX3 exerts potent activities against Ascosphaera apis (a deadly brood pathogen) likely stemming from in-hive dispersal differences, whereas patty-LX3 promotes synergistic brood development via unique nutritional benefits. These findings provide a foundational basis for spray-based probiotic application in apiculture and collectively highlight the importance of considering delivery method in disease management strategies.
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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".