<i>Drosophila lowfat</i>, a novel modulator of Fat signaling
Bibliographic record
Abstract
The Fat-Hippo-Warts signaling network regulates both transcription and planar cell polarity. Despite its crucial importance to the normal control of growth and planar polarity, we have only a limited understanding of the mechanisms that regulate Fat. We report here the identification of a conserved cytoplasmic protein, Lowfat (Lft), as a modulator of Fat signaling. Drosophila Lft, and its human homologs LIX1 and LIX1-like, bind to the cytoplasmic domains of the Fat ligand Dachsous, the receptor protein Fat, and its human homolog FAT4. Lft protein can localize to the sub-apical membrane in disc cells, and this membrane localization is influenced by Fat and Dachsous. Lft expression is normally upregulated along the dorsoventral boundary of the developing wing, and is responsible for elevated levels of Fat protein there. Levels of Fat and Dachsous protein are reduced in lft mutant cells, and can be increased by overexpression of Lft. lft mutant animals exhibit a wing phenotype similar to that of animals with weak alleles of fat, and lft interacts genetically with both fat and dachsous. These studies identify Lft as a novel component of the Fat signaling pathway, and the Lft-mediated elevation of Fat levels as a mechanism for modulating Fat signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".