Protein signaling pathways in differentiation of neural stem cells
Bibliographic record
Abstract
Neural stem cells (NSC) capable of differentiating into neurons, astrocytes and oligodendrocytes are a promising source of cells for the treatment of central nervous system diseases. Access to signaling proteins present in such cells in low copies and with specific regulatory functions has been very restrictive until now as judged by classical proteomic approaches and limitations due to scarcity of stem cell populations. Hence, we utilized the Kinex Antibody Microarray analysis where profiles of the proliferating porcine NSC and differentiated counterparts were compared and selected changes were verified by immunoblotting. Differentiated neural cells exhibited an increased level of RafB proto-oncogene-encoded protein-serine kinase, MAP kinase protein-serine kinase 3, heme oxygenase 2 (HO2) and protein phosphatase 4 catalytical subunit. On the other hand, relatively high level of G protein-coupled receptor-serine kinase 2 and enhanced phosphorylations of alphaB-crystallin (S45), protein-serine kinase C gamma (T655), protein-serine kinase D (PKCmu; S738+S742) together with eukaryotic translation initiation factor 2 alpha (eIF2alpha) (S51) raised intriguing questions as regards their potential functionality within stem cells. In-depth study of HO2 and phospho-S45 alphaB-crystallin confirmed expression profiles and intense cytoplasmic localization of HO2 in neurons but a weaker signal in glial cells. Phospho-S45 alphaB-crystallin was localized in nuclei of differentiated neural cells. Computer simulation of possible interaction network connecting regulated proteins, exposed additional relationships including direct interactions of HO2 with amyloid precursor protein or huntingtin-associated protein 1.
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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".