Signaling Within a Caveolae‐Like Membrane Microdomain in Human Neuroblastoma Cells in Response to Fibroblast Growth Factor
Bibliographic record
Abstract
It is now clear that the plasma membrane is not homogeneous but contains specific subcompartments characterized by their unique lipid and protein composition. Based on their enrichment in various signaling molecules, these microcompartments are now recognized to be sites of localized signal transduction for several extracellular stimuli. At least two different types of microdomains can be identified, largely based on the presence or absence of the caveolin proteins. The generic name of caveolae-like domains is commonly used to refer to both domains indistinguishably. Although caveolin proteins were long thought to be absent from the brain, we have shown that the human neuroblastoma cell line LAN-1 expresses both caveolin-1 and caveolin-2. Basic fibroblast growth factor (FGF)-2 induced a specific signaling response within the caveolae-like domain of LAN-1 cells, characterized by the tyrosine phosphorylation of a 75-80-kDa protein. This protein present in the caveolae-like domains has properties suggesting that it is a member of the SNT family of adapter proteins. The signaling event originating in the caveolae-like domains in response to FGF-2 appeared to require the activation of at least Fyn and Lyn, two members of the Src family of tyrosine kinases. This work suggests that compartmentalized signaling within caveolae-like domains may create a level of specificity for certain growth factors such as FGF.
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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".