Differential Gene Expression Associated with Honey Bee Grooming Behavior in Response to Varroa Mites
Bibliographic record
Abstract
Honey bee (Apis mellifera) grooming behavior is an important mechanism of resistance against the parasitic mite Varroa destructor. This research was conducted to study associations between grooming behavior and the expression of selected immune, neural, detoxification, developmental and health-related genes. Individual bees tested in a laboratory assay for various levels of grooming behavior in response to V. destructor were also analyzed for gene expression. Intense groomers (IG) were most efficient in that they needed significantly less time to start grooming and fewer grooming attempts to successfully remove mites from their bodies than did light groomers (LG). In addition, the relative abundance of the neurexin-1 mRNA, was significantly higher in IG than in LG, no groomers (NG) or control (bees without mite). The abundance of poly U binding factor kd 68 and cytochrome p450 mRNAs were significantly higher in IG than in control bees. The abundance of hymenoptaecin mRNA was significantly higher in IG than in NG, but it was not different from that of control bees. The abundance of vitellogenin mRNA was not changed by grooming activity. However, the abundance of blue cheese mRNA was significantly reduced in IG compared to LG or NG, but not to control bees. Efficient removal of mites by IG correlated with different gene expression patterns in bees. These results suggest that the level of grooming behavior may be related to the expression pattern of vital honey bee genes. Neurexin-1, in particular, might be useful as a bio-marker for behavioral traits in bees.
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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".