Genetic Bases Of Aposematic Traits: Insights from the Skin Transcriptional Profiles of <i>Oophaga</i> Poison Frogs
Bibliographic record
Abstract
A bstract Aposematic organisms advertise their defensive toxins to predators using a variety of warning signals, including bright coloration. While most Neotropical poison frogs (Dendrobatidae) rely on crypsis to avoid predators, Oophaga poison frogs from South America advertise their chemical defenses, a complex mix of diet-derived alkaloids, by using conspicuous hues. The present study aimed to characterize the skin transcriptomic profiles of the South American clade of Oophaga poison frogs ( O. anchicayensis, O. solanensis, O. lehmanni and O. sylvatica ). Our analyses showed very similar transcriptomic profiles for these closely related species in terms of functional annotation and relative abundance of gene ontology terms expressed. Analyses of expression profiles of Oophaga and available skin transcriptomes of cryptic anurans allowed us to propose possible mechanisms for the active sequestration of alkaloid-based chemical defenses and to highlight some genes that may be potentially involved in resistance mechanisms to avoid self-intoxication and skin coloration. In doing so, we provide an important molecular resource for the study of warning signals that will facilitate the assembly and annotation of future poison frog genomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".