Functional Characterization Supports Multiple Evolutionary Origins of Pheromone Receptors in Bark Beetles
Bibliographic record
Abstract
Chemical communication using pheromones is thought to have contributed to the diversification and speciation of insects. The species-specific pheromones are detected by specialized pheromone receptors (PRs). Whereas the evolution and function of PRs have been extensively studied in Lepidoptera, only a few PRs have been identified in beetles, which limits our understanding of their evolutionary histories and physiological functions. To shed light on these questions, we aimed to functionally characterize potential PRs in the spruce bark beetle Ips typographus ("Ityp") and explore their evolutionary origins and molecular interactions with ligands. Males of this species release an aggregation pheromone comprising 2-methyl-3-buten-2-ol and (4S)-cis-verbenol, which attracts both sexes to attacked trees. Using two systems for functional characterization, we show that the highly expressed odorant receptor (OR) ItypOR41 responds specifically to (4S)-cis-verbenol, with structurally similar compounds eliciting minor responses. We next targeted the closely related ItypOR40 and ItypOR45. Whereas ItypOR40 was unresponsive, ItypOR45 showed an overlapping response profile with ItypOR41, but a broader tuning. Our phylogenetic analysis shows that these ORs are present in a different OR clade as compared to all other known beetle PRs, suggesting multiple evolutionary origins of PRs in bark beetles. Next, using computational analyses and experimental validation, we reveal two amino acid residues (Gln179 and Trp310) that are important for ligand binding and pheromone specificity of ItypOR41 for (4S)-cis-verbenol, possibly via hydrogen bonding to Gln179. Collectively, our results shed new light on the origins, specificity, and ligand binding mechanisms of PRs in beetles.
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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".