ARS-Interacting Multi-Functional Protein 1 Induces Proliferation of Human Bone Marrow-Derived Mesenchymal Stem Cells by Accumulation of β-Catenin via Fibroblast Growth Factor Receptor 2-Mediated Activation of Akt
Bibliographic record
Abstract
ARS-Interacting Multi-functional Protein 1 (AIMP1) is a cytokine that is involved in the regulation of angiogenesis, immune activation, and fibroblast proliferation. In this study, fibroblast growth factor receptor 2 (FGFR2) was isolated as a binding partner of AIMP peptide (amino acids 6-46) in affinity purification using human bone marrow-derived mesenchymal stem cells (BMMSCs). AIMP1 peptide induced the proliferation of adult BMMSCs by activating Akt, inhibiting glycogen synthase kinase-3β, and thereby increasing the level of β-catenin. In addition, AIMP1 peptide induced the translocation of β-catenin to the nucleus and increased the transcription of c-myc and cyclin D1 by activating the β-catenin/T-cell factor (TCF) complex. By contrast, transfection of dominant negative TCF abolished the effect of AIMP1. The inhibition of Akt, using LY294002, abolished the accumulation and nuclear translocation of β-catenin induced by AIMP1, leading to a decrease in c-myc and cyclin D1 expression, which decreased the proliferation of BMMSCs. An intraperitoneal injection of AIMP1 peptide into C57/BL6 mice increased the colony formation of fibroblast-like cells. Fluorescence activated cell sorting analysis showed that the colony-forming cells were CD29(+)/CD44(+)/CD90(+)/CD105(+)/CD34(-)/CD45(-), which is characteristic of MSCs. In addition, the fibroblast-like cells differentiated into adipocytes, chondrocytes, and osteocytes. Taken together, these data suggest that AIMP1 peptide promotes the proliferation of BMMSCs by activating the β-catenin/TCF complex via FGFR2-mediated activation of Akt, which leads to an increase in MSCs in peripheral blood.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".