The endosomal adaptor protein APPL1 impairs the turnover of leading edge adhesions to regulate cell migration
Bibliographic record
Abstract
Cell migration is a complex process that requires the integration of signaling events that occur in distinct locations within the cell. Adaptor proteins, which can localize to different subcellular compartments, where they bring together key signaling proteins, are emerging as attractive candidates for controlling spatially coordinated processes. However, their function in regulating cell migration is not well understood. In this study, we demonstrate a novel role for the adaptor protein containing a pleckstrin-homology (PH) domain, phosphotyrosine-binding (PTB) domain, and leucine zipper motif 1 (APPL1) in regulating cell migration. APPL1 impairs migration by hindering the turnover of adhesions at the leading edge of cells. The mechanism by which APPL1 regulates migration and adhesion dynamics is by inhibiting the activity of the serine/threonine kinase Akt at the cell edge and within adhesions. In addition, APPL1 significantly decreases the tyrosine phosphorylation of Akt by the nonreceptor tyrosine kinase Src, which is critical for Akt-mediated cell migration. Thus, our results demonstrate an important new function for APPL1 in regulating cell migration and adhesion turnover through a mechanism that depends on Src and Akt. Moreover, our data further underscore the importance of adaptor proteins in modulating the flow of information through signaling pathways.
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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 | 0.001 |
| 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".