Differential gene expression mediates physiological responses to perceived predation risk in a developmentally plastic vertebrate, the northern leopard frog (Lithobates pipiens)
Bibliographic record
Abstract
Prey can respond to predation risk through developmental plasticity, generating anti-predator phenotypes. These inducible defenses arise from changes to the stress axis, and neuroendocrine-triggered gene regulation is a likely mechanism influencing such phenotypes. As tadpoles, amphibians improve their escape performance by modifying tail shape in response to perceived predation risk (PPR), and this process should involve tissue and developmentally specific gene regulation. We exposed Lithobates pipiens tadpoles to PPR from Aeshnidae predators and measured tail morphology and transcriptomic response across different tissues (head and tail) and development (pre-metamorphosis to pro-metamorphosis). We found that PPR induced plasticity in tail shape, and this response was suppressed when tadpoles were also exposed to a glucocorticoid synthesis inhibitor. Differential gene expression was associated with predation stress across head and tail tissue, and developmental stage. Predator-exposed tadpoles exhibited up-regulation of genes responsible for muscle tissue and nervous system development, primarily in tail tissue and in pre-metamorphosis. PPR broadly influenced pathways across tissues and metamorphosis, including developmental, endocrine, and immune system pathways. This study provides an important step in understanding transcriptomic responses during predator induced morphological change, and demonstrates that gene expression, as induced by perceived predation risk, is a prominent mechanism of developmental plasticity.
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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".