In sickness and in health: RNA-Seq finds viruses associated with mutualist quality in the Amazonian plant-ant <i>Allomerus octoarticulatus</i>
Bibliographic record
Abstract
Ant-plant symbioses are classic examples of mutualism in which ant "bodyguards" defend myrmecophytic plants against enemies in exchange for nest sites and often food. We used RNA-Seq to profile the transcriptomes of Allomerus octoarticulatus ant workers, which aggressively defend the Amazonian plant Cordia nodosa against herbivores, but to varying degrees. Field behavioral assays with herbivores in the Peruvian Amazon showed striking variation among colonies in the relative zeal with which A. octoarticulatus workers defend their host plant. Highly effective and ineffective bodyguards differed in their gene expression profiles, which revealed viral infections significantly associated with ant bodyguarding behavior. Transcripts from eight new positive-sense single-stranded RNA viruses were differentially expressed between colonies with high- or low-quality bodyguards. Colonies of high- and low-quality bodyguards were infected by distinct viruses, including viruses clustering phylogenetically with viruses known to cause aggression or reduced locomotion, respectively, in bees. Gene expression, including of immunity-related genes, also differed between broodcare workers and bodyguard ants, suggesting bodyguarding is a distinct worker task. Ant colony health and viral infections may influence ant cooperation with plants in ant-plant mutualisms.
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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".