The Fat/Hippo pathway drives photoperiod-induced wing length polyphenism
Bibliographic record
Abstract
Abstract Identifying the genetic mechanisms that translate information from the environment into developmental programs to control size, shape and color are important for gaining insights into adaptation to changing environments. Insect polyphenisms provide good models to study such mechanisms because environmental factors are the main source of trait variation. Here we studied the genetic mechanism that controls photoperiod-induced wing length polyphenism in the water strider Gerris buenoi . By sequencing RNA sampled from wing buds across developmental stages under different photoperiodic conditions known to trigger alternative wing developmental trajectories, we found that differences in transcriptional activity arose primarily in the late 5 th instar stage. Among the differentially expressed genes, the Fat/Hippo and ecdysone signaling pathways, both putative growth regulatory mechanisms showed significant enrichment. We used RNA interference against the differentially expressed genes Fat, Dachsous and Yorkie to assess whether they play a causative role in photoperiod induced wing length variation in Gerris buenoi . Our results show that the conserved Fat/Hippo pathway is a key regulatory network involved in the control of wing polyphenism in this species. This study provides an important basis for future comparative studies on the evolution of wing polyphenism and significantly deepens our understanding of the genetic regulation of insect polyphenisms.
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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".