Caste- and environment-associated differential expression of olfactory receptors in pest ants
Bibliographic record
Abstract
As human society continues to grow and evolve, so does the need for effective pest management strategies. Olfactory-mediated control methods, such as attractant and repellent compounds, are a proposed strategy for mitigating the damaging effects of some insect pests, most notably ants, that rely on olfaction for communication. To develop such compounds, it is first important to comprehensively understand the target species' olfactory transcriptome in order to guide future targeted functional characterization of relevant olfactory proteins. Here, we perform bulk RNA-seq analysis of antennae from three notable pest ant species, Camponotus floridanus, Atta sexdens, and Atta cephalotes. Specifically, we highlight the expression profiles of olfactory receptor genes, as they may serve as potential targets of future industry research and application. We find that the ant antennal transcriptome differs between each species' castes, potentially reflecting varying behaviors and tasks, and also appears to be influenced by the surrounding environment. Our findings suggest a general up-regulation of olfactory receptor genes amongst foraging castes, also demonstrating that, when comparing foraging ants from differing environments, olfactory-related genes exhibit considerable patterns of differential expression. These findings suggest variable olfactory sensitivity depending on the aforementioned factors, warranting further investigation into whether differing caste and environmental conditions may negatively influence the effectiveness of broad-range olfactory-mediated pest management strategies. Development of pest management tools that target specific groups of insects by environment or caste may lead to more effective control.
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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".