Upregulation of MT1-MMP Expression by Hyaluronic Acid Enhances Homing-Related Responses of Hematopoietic CD34+ Cells to an SDF-1 Gradient.
Bibliographic record
Abstract
Abstract Hyaluronic acid (HA), the bone marrow (BM) extracellular matrix microenvironment (ECM) component, not only supports cell adhesion but also promotes migration and homing of hematopoietic stem/progenitor cells (HSPC) by interacting with its cell surface receptor CD44. CD44 has been shown to co-localize with matrix metalloproteinases (MMPs), particularly membrane-type (MT)-1 MMP and MMP-9, at the leading edge of migrating tumor cells, and the cleavage of CD44 by MT1-MMP is critical for tumor cell migration and invasion. MT1-MMP has strong pericellular proteolytic activity and also activates latent forms of MMP-2 and MMP-9. In this study we examined the effect of HA on MT1-MMP expression and migration of BM, peripheral blood and cord blood CD34+ cells. We found that HA upregulates mRNA for MT1-MMP and MMP-9, increases MT1-MMP protein (as evaluated by Western blotting) and stimulates MMP-9 and MMP-2 activity (as determined by zymography) in CD34+ cells. In chemotaxis assays HA alone did not show any chemotactic activity but primed the chemotaxis of CD34+ cells to a low SDF-1 gradient (10 ng/mL) and their trans-Matrigel chemoinvasion to a low SDF-1 gradient. Similarly, SDF-1 besides stimulating MMP-2 and MMP-9 (as we previously described in Exp Hematol2000; 28:1274), also upregulated MT1-MMP in CD34+ cells. In addition, we found that the HA enhanced activation of latent MMP-2 in co-cultures of CD34+ cells with HUVEC. In conclusion, we demonstrate for the first time that (i) HA primes CD34+ cell chemotaxis and chemoinvasion to a low SDF-1 gradient; (ii) that both HA and SDF-1 stimulate MT1-MMP, MMP-2 and MMP-9 in CD34+ cells, and (iii) HA enhances activation of proMMP-2 in the ECM. Hence the interaction of HA and SDF-1 with MT1-MMP could play an important role in HSPC migration and homing and we postulate that pretreatment of HSPC with HA before transplantation could enhance their homing by inducing expression of MMPs that is optimal for stem cell engraftment.
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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".