The SHCA adapter protein cooperates with lipoma-preferred partner in the regulation of adhesion dynamics and invadopodia formation
Bibliographic record
Abstract
SHC adaptor protein (SHCA) and lipoma-preferred partner (LPP) mediate transforming growth factor β (TGFβ)-induced breast cancer cell migration and invasion. Reduced expression of either protein diminishes breast cancer lung metastasis, but the reason for this effect is unclear. Here, using total internal reflection fluorescence (TIRF) microscopy, we found that TGFβ enhanced the assembly and disassembly rates of paxillin-containing adhesions in an SHCA-dependent manner through the phosphorylation of the specific SHCA tyrosine residues Tyr-239, Tyr-240, and Tyr-313. Using a BioID proximity labeling approach, we show that SHCA exists in a complex with a variety of actin cytoskeletal proteins, including paxillin and LPP. Consistent with a functional interaction between SHCA and LPP, TGFβ-induced LPP localization to cellular adhesions depended on SHCA. Once localized to the adhesions, LPP was required for TGFβ-induced increases in cell migration and adhesion dynamics. Mutations that impaired LPP localization to adhesions (mLIM1) or impeded interactions with the actin cytoskeleton via α-actinin (ΔABD) abrogated migratory responses to TGFβ. Live-cell TIRF microscopy revealed that SHCA clustering at the cell membrane preceded LPP recruitment. We therefore hypothesize that, in the presence of TGFβ, SHCA promotes the formation of small, dynamic adhesions by acting as a nucleator of focal complex formation. Finally, we defined a previously unknown function for SHCA in the formation of invadopodia, a process that also required LPP. Our results reveal that SHCA controls the formation and function of adhesions and invadopodia, two key cellular structures required for breast cancer metastasis.
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".