The Maf factor Traffic jam both enables and inhibits collective cell migration in<i>Drosophila</i>oogenesis
Bibliographic record
Abstract
Border cell cluster (BCC) migration in the Drosophila ovary is an excellent system to study the gene regulatory network that enables collective cell migration. Here, we identify the large Maf transcription factor Traffic jam (Tj) as an important regulator of BCC migration. Tj has a multifaceted impact on the known core cascade that enables BCC motility, consisting of the Jak/Stat signaling pathway, the C/EBP factor Slow border cells (Slbo), and the downstream effector DE-cadherin (DEcad). The initiation of BCC migration coincides with a Slbo-dependent decrease in Tj expression. This reduction of Tj is required for normal BCC motility, as high Tj expression strongly impedes migration. At high concentration, Tj has a tripartite negative effect on the core pathway: a decrease in Slbo, an increase in the Jak/Stat inhibitor Socs36E, and a Slbo-independent reduction of DEcad. However, maintenance of a low expression level of Tj in the BCC during migration is equally important, as loss of tj function also results in a significant delay in migration concomitant with a reduction of Slbo and consequently of DEcad. Taken together, we conclude that the regulatory feedback loop between Tj and Slbo is necessary for achieving the correct activity levels of migration-regulating factors to ensure proper BCC motility.
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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".