The transcriptomics of phenotypic nonspecificity in <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Transcription factor (TF) function is redundant: TF phenotypes are frequently rescued by TFs not resident to the TF locus, a phenomenon termed phenotypic nonspecificity. Phenotypic nonspecificity in Drosophila melanogaster is not dependent on the DNA binding specificity of the TFs and generally due to genetic complementation. Two TF phenotypes (doublesex [dsx] and apterous [ap]) are rescued by multiple TFs. The rescue by resident TFs (DSXF or AP) and the rescue and non-rescue by nonresident TFs of these 2 phenotypes were used to distinguish between 3 possible outcomes of the comparison of the TF-dependent mRNA accumulation in these 2 systems. First, the sets of TF-dependent mRNAs are independent and nonoverlapping; second, the sets of TF-dependent mRNAs are independent and overlapping; and third, the sets of TF-dependent mRNAs are constrained and have extensive overlap. The transcriptomes associated with rescue by resident TFs, and rescue and non-rescue by nonresident TFs, of the 2 TF phenotypes (dsx and ap) provided many examples of extensive overlap indicating regulation of constrained sets of genes. However, the strength of correlation of transcript accumulation observed between the resident and nonresident TFs was not a strong predictor for rescue of the phenotype by the nonresident TFs. The accumulation of a constrained set of mRNAs is discussed in relation to 3 potential explanations of phenotypic nonspecificity: limited specificity of TF function, the hypothetical assembly of TFs into wolfpacks, and chromatin accessibility.
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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".