Nucleotides metabolizing ectoenzymes as possible markers of mesenchymal stem cell osteogenic differentiation
Bibliographic record
Abstract
Growing murine mesenchymal stem cells (mMSCs) from mouse bone marrow decreased their rate of proliferation in the presence of benzoylbenzoyl-ATP persistently, but the inhibitory effect of ATP was strong only in a concentration of 50 μmol·L(-1) and lasted for 48 h in culture. These results hinted at ATP hydrolysis by the cell surface enzymes at the lower concentrations and thus it may be not able to inhibit MSCs. By using ATP, ADP, or AMP as substrates, we tested the ectonucleotidase activity on the surface of undifferentiated MSCs and MSC-derived osteoblasts. Here, we report that although nucleoside triphosphate diphosphohydrolase (NTPDase)1 and NTPDase8 are engaged in the metabolism of ATP in MSC-derived osteoblasts, NTPDase3 is responsible for its metabolism in undifferentiated MSCs. In this study, we also realized that osteoblasts effectively metabolize ADP to ATP and AMP. The enzymatic activity of adenylate kinase (AK) is consistent with the high expression level of the AK gene. Therefore, it was tempting to suggest that this enzyme, together with NTPDase1 and NTPDase8, assume the role of specific markers that allowed distinction between differentiated osteoblasts and early undifferentiated MSCs. Additionally, unlike osteoblasts, undifferentiated MSCs demonstrated the activity of 5'-nucleotidase (CD73). However, the expression analysis of CD73 mRNA did not show any differences; CD73 mRNA was expressed in both kinds of cells to the same extent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".