Effects of Altering Insulin Signaling Pathway Genes on Sex Specific Growth in Drosophila melanogaster
Bibliographic record
Abstract
Introduction: The mechanisms of growth and development are becoming better understood by studying Drosophila melanogaster. Although the presence of two major nutrient-sensing pathways and their intracellular signaling molecules have been identified, sex-specific effects of altering these pathways are poorly understood. This study aims to confirm the expression patterns of 5 GAL4 strains (r4, PPL, Lsp, da, nubbin) with green fluorescent protein (GFP), and observe the sex-specific phenotypic responses in a tissue-specific and systemic manner. Methods: Each GAL4 strain was crossed with a UAS-GFP NLS (upstream activation sequence-nuclear localization sequence) reporter to view GFP expression patterns and to UAS-insulin receptor dominant negative (InR DN) and constitutively active (InR CA) reporters to assess phenotypic response. Results: It was observed that all 5 GAL4 strains exhibited expression patterns consistent with their tissue specific promoters. In addition, when all 5 GAL4 strains were crossed with the UAS-InR DN and UAS-InR CA, sex-specific phenotypic responses were observed in terms of tissue-specific and systemic growth, by measuring wing size and thorax length, respectively. Discussion: Confirming the expression patterns of all 5 GAL4 strains is necessary when looking at tissue-specific and systemic phenotypic responses, as it ensures that phenotypic responses are due to altering of InR and not of non-functioning GAL4 strains themselves. Interestingly, the mean thorax lengths for the InR CA were consistently smaller than the InR DN for all GAL4 strains. Conclusion: Although this study found promising results, more research is required to truly understand sexual size dimorphism in growth patterns. A next step is using the UAS-GAL4 system to alter genes of other signaling molecules within the IIS or TOR pathway. By looking at different key players within the pathway, we can understand how all these molecules work together and which ones have a greater sex-specific effect.
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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) | 0.002 | 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".