Single molecule network analysis identifies structural changes to caveolae and scaffolds due to mutation of the caveolin-1 scaffolding domain
Bibliographic record
Abstract
Caveolin-1 (CAV1), the caveolae coat protein, also associates with non-caveolar scaffold domains. Single molecule localization microscopy (SMLM) network analysis distinguishes caveolae and three scaffold domains, hemispherical S2 scaffolds and smaller S1B and S1A scaffolds. The caveolin scaffolding domain (CSD) is a highly conserved hydrophobic region that mediates interaction of CAV1 with multiple effector molecules. F92A/V94A mutation disrupts CSD function, however the structural impact of CSD mutation on caveolae or scaffolds remains unknown. Here, SMLM network analysis quantitatively shows that expression of the CAV1 CSD F92A/V94A mutant in CRISPR/Cas CAV1 knockout MDA-MB-231 breast cancer cells reduces the size and volume and enhances the elongation of caveolae and scaffold domains, with more pronounced effects on S2 and S1B scaffolds. Convex hull analysis of the outer surface of the CAV1 point clouds confirms the size reduction of CSD mutant CAV1 blobs and shows that CSD mutation reduces volume variation amongst S2 and S1B CAV1 blobs at increasing shrink values, that may reflect retraction of the CAV1 N-terminus towards the membrane, potentially preventing accessibility of the CSD. Detection of point mutation-induced changes to CAV1 domains highlights the utility of SMLM network analysis for mesoscale structural analysis of oligomers in their native environment.
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 | 0.001 | 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".