Chaperonin 10 as an endothelial‐derived differentiation factor: Role of glycogen synthase kinase‐3
Bibliographic record
Abstract
The anti-inflammatory peptide early pregnancy factor/chaperonin 10 (cpn10) was identified by 2D-electrophoresis/mass spectrometry as one of the proteins increased in human umbilical cord endothelial cells (HUVEC) after treatment with erythropoietin (EPO). EPO increased the amount of cpn10 released into the medium of HUVEC cultures, despite the absence of a secretory signal peptide. Although immunosupressive agents would represent an indirect advantage for red cell formation under conditions of infection and inflammation, it is possible that cpn10 may have direct effects on erythroid cells. We show that the chaperonin decreased cell proliferation in cultures of the erythroleukemia cell line K562 and increased the amounts of the erythroid differentiation markers glycophorin A and hemoglobin in TF-1 cells. Nevertheless, cpn10 is not a specific erythroid cell differentiation factor, because monolayers of skin fibroblasts overexpressing cpn10 had significantly higher levels of the differentiation marker collagen I than normal fibroblasts. Nothing is known about the mechanism of action of cpn10 in its capacity as a general differentiation factor. An analysis of early changes taking place in K562 cells after incubation with cpn10 using antibody microarrays identified several phosphorylation events, including a decrease of GSK-3alpha phosphorylation. Further studies in TF-1 cells indicated that cpn10 decreased the phosphorylation of cofilin-1 while stimulating that of GSK-3beta. Furthermore, glycophorin A production decreased in the presence of a GSK-3 inhibitor in the same cells. These experiments support the idea that GSK-3-regulated signal transduction pathways are not only important for stem cell maintenance but may be involved in events controlling cell differentiation.
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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".