Cytoskeleton Interactions Involved in the Assembly and Function of Glycoprotein-80 Adhesion Complexes in Dictyostelium
Bibliographic record
Abstract
Adhesion complexes typically assemble from clustered receptors that link to the cytoskeleton via cytoplasmic adapter proteins. However, it is unclear how phospholipid-anchored adhesion molecules, such as the Dictyostelium receptor gp80, interact with the cytoskeleton. gp80 has been found to form adhesion complexes from raftlike membrane domains, which can be isolated as a Triton X-100-insoluble floating fraction (TIFF). We report here that the actin-binding protein ponticulin mediates TIFF-cytoskeleton interactions. Analysis of gp80-null cells revealed that these interactions were minimal in the absence of gp80. During development, gp80 was required to enhance these interactions as its adhesion complexes assembled. Whereas ponticulin and gp80 could partition independently into TIFF, gp80 was shown to recruit ponticulin to cell-cell contacts and to increase its partitioning into TIFF. However, these proteins did not co-immunoprecipitate. Furthermore, sterol sequestration abrogated the association of ponticulin with TIFF without affecting gp80, suggesting that sterols may mediate the interactions between ponticulin and gp80. In ponticulin-null cells, large gp80 adhesion complexes assembled in the absence of ponticulin despite the lack of cytoskeleton association. We propose that such nascent gp80 adhesion complexes produce expanded raftlike domains that recruit ponticulin and thereby establish stable cytoskeleton interactions to complete the assembly process.
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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) | 0.000 | 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".