Feeding-Related Traits Are Affected by Dosage of the<i>foraging</i>Gene in<i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Abstract Nutrient acquisition and energy storage are critical parts of achieving metabolic homeostasis. The foraging gene in Drosophila melanogaster has previously been implicated in multiple feeding-related and metabolic traits. Before foraging’s functions can be further dissected, we need a precise genetic null mutant to definitively map its amorphic phenotypes. We used homologous recombination to precisely delete foraging, generating the for0 null allele, and used recombineering to reintegrate a full copy of the gene, generating the {forBAC} rescue allele. We show that a total loss of foraging expression in larvae results in reduced larval path length and food intake behavior, while conversely showing an increase in triglyceride levels. Furthermore, varying foraging gene dosage demonstrates a linear dose-response on these phenotypes in relation to foraging gene expression levels. These experiments have unequivocally proven a causal, dose-dependent relationship between the foraging gene and its pleiotropic influence on these feeding-related traits. Our analysis of foraging’s transcription start sites, termination sites, and splicing patterns using rapid amplification of cDNA ends (RACE) and full-length cDNA sequencing, revealed four independent promoters, pr1–4, that produce 21 transcripts with nine distinct open reading frames (ORFs). The use of alternative promoters and alternative splicing at the foraging locus creates diversity and flexibility in the regulation of gene expression, and ultimately function. Future studies will exploit these genetic tools to precisely dissect the isoform- and tissue-specific requirements of foraging’s functions and shed light on the genetic control of feeding-related traits involved in energy homeostasis.
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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 | 0.001 | 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) | 0.001 | 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".