Comprehensive Annotation of Olfactory and Gustatory Receptor Genes and Transposable Elements Revealed Their Evolutionary Dynamics in Aphids
Bibliographic record
Abstract
Gene duplication and transposable elements (TEs) are major drivers of genomic innovation that can fuel adaptation. While the roles of duplication and TE-driven diversification are documented in plant pathogens, they remain insufficiently explored in insect pests such as aphids, where olfactory (OR) and gustatory receptor (GR) genes are key to host recognition. We analyzed 521 OR and 399 GR genes, alongside TEs, across 12 aphid genomes with varying host ranges. Aphid lineages with broader host ranges exhibited higher evolutionary rates, driven by gene family expansions linked to host interaction, including lipid metabolism, immune function, and transposase activity. OR and GR genes evolved through proximal and tandem duplications and were shaped by diversifying selection, with bursts of positive selection followed by prolonged purifying selection, consistent with adaptation to novel hosts. Younger TEs were significantly enriched near OR genes compared to GRs and other genomic regions, suggesting a catalytic role of TEs in their diversification. However, OR proteins encoded by TE-associated ORs exhibited reduced functional potential. In contrast, GR proteins encoded by TE-associated GRs retained signatures of adaptation, as inferred from deep learning models predicting functionally important protein regions. These findings suggest that TE activity may facilitate functional innovation in GRs while alleviating constraints or pseudogenization in ORs. This study reveals how duplication, selection, and TE dynamics shape gene evolution in insect pests. It also provides the first chromosome-scale genome assembly of Dysaphis plantaginea, with comprehensive annotations and functional predictions of OR/GR genes, bridging adaptive evolution with mechanistic insights.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".