Keeping an Eye on Plasticity Genes: Insulin/TOR Pathway Components Mediating Nutritional Plasticity of Eyes Within and Between Sex
Bibliographic record
Abstract
ABSTRACT The eyes of insects exhibit extreme morphological variation through changes in size and shape. While the model organism Drosophila melanogaster has been used to elucidate the underlying gene networks and cell-signalling pathways that regulate the patterning and growth of the eye, insight into the regulators of nutrient-dependent growth and allometry within and between the sexes remains poorly understood. Here we show that perturbations of different nodes of the Insulin/TOR pathway modulate the impact of nutritional variation on the growth of the eye to varying degrees, and that this is further influenced by sex. When starved, wild-type flies decrease in eye size and body size isometrically in females, yet males change eye-to-body size allometrically. Subsequently we used eye-specific RNAi to knockdown each component of the Insulin/TOR pathway to characterize the influence each component has in modulating nutritional plasticity of the sizing properties of the eye. Surprisingly, this resulted in a range of size and scaling variation, components modulating the plastic response on eye size mean, eye-to-body slope, intercept, as well as eye size variance, some effects ranging in magnitude from shutting off plasticity to amplifying, and some which were sex-limited. Therefore, components of the Insulin/TOR pathway vary in their degree and ability to influence the effect of nutritional variation on eye growth within and between sexes in terms of average size, allometry at both the level of intercept and slope, as well as the degree of variance. More generally, the morphospace and allometry of a trait can evolve within and across the sexes through modifications of plasticity genes that mediate gene-by-environment interactions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".