Inhibition of T cell protein tyrosine phosphatase enhances IL-18-dependent hematopoietic stem cell expansion (P6268)
Bibliographic record
Abstract
Abstract The clinical application of hematopoietic progenitor cell-based therapies for the treatment of hematological diseases is hindered by current protocols, which are cumbersome and have limited efficacy to augment the progenitor cell pool. We report that inhibition of T cell protein tyrosine phosphatase (TC-PTP), an enzyme involved in the regulation of cytokine signaling, through gene knockout results in a 9-fold increase in the number of hematopoietic progenitors in murine bone marrow. This effect could be reproduced using a short (48 h) treatment with a pharmacological inhibitor of TC-PTP in murine bone marrow, as well as in human bone marrow peripheral blood and cord blood. We also demonstrate that the ex-vivo use of TC-PTP inhibitor only provides a temporary effect on stem cells and did not alter their capacity to reconstitute all hematopoietic components in vivo. We establish that one of the mechanisms whereby inhibition of TC-PTP mediates its effects involves the IL-18 signaling pathway, leading to increased production of IL-12 and IFN-gamma by progenitor cells. Together, our results reveal a previously unrecognized role for IL-18 in contributing to the augmentation of the stem cell pool, and provide a novel and simple method to rapidly expand progenitor cells from a variety of sources using a pharmacological compound.
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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) | 0.001 | 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".