Leukocyte-specific protein 1 targets the ERK/MAP kinase scaffold protein KSR and MEK1 and ERK2 to the actin cytoskeleton
Bibliographic record
Abstract
The identification and characterization of scaffold and targeting proteins of the ERK/MAP kinase pathway is important to understand the function and intracellular organization of this pathway. The F-actin binding protein leukocyte-specific protein 1 (LSP1) binds to PKCbetaI and expression of B-LSP1, an LSP1 truncate containing the PKCbetaI binding residues, inhibits anti-IgM-induced translocation of PKCbetaI to the plasma membrane and anti-IgM-induced activation of ERK2. To understand the role of LSP1 in the regulation of PKCbetaI-dependent ERK2 activation, we investigated whether LSP1 interacts with ERK/MAP kinase pathway components and targets these proteins to the actin cytoskeleton. We show that LSP1 associates with the ERK scaffold protein KSR and with MEK1 and ERK2. LSP1-associated MEK1 is activated by anti-IgM treatment and this activation is inhibited by expression of B-LSP1, suggesting that the activation of LSP1-associated MEK1 is PKCbetaI dependent. Confocal microscopy showed that LSP1 targets KSR, MEK1 and ERK2 to peripheral actin filaments. Thus our data show that LSP1 is a cytoskeletal targeting protein for the ERK/MAP kinase pathway and support a model in which MEK1 and ERK2 are organized in a cytoskeletal signaling complex together with KSR, PKCbetaI and LSP1 and are activated by anti-IgM in a PKCbetaI-dependent manner.
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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) | 0.003 | 0.001 |
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".