Targeted deletion of the tachykinin 4 gene (TAC4−/−) influences the early stages of B lymphocyte development
Bibliographic record
Abstract
Hemokinin-1 (HK-1), encoded by the TAC4 gene, is a tachykinin peptide that is predominantly expressed in non-neuronal cells, such as immune cells. We have disrupted the mouse TAC4 gene to obtain a better understanding of the actions of HK-1 during hematopoiesis. We demonstrate here that TAC4(-/-) mice exhibit an increase of CD19(+)CD117(+)HSA(+)BP.1(-) "fraction B" pro-B cells in the bone marrow, whereas pre-B, immature, and mature B cells are within the normal range. We show that in vitro cultures derived from TAC4(-/-) bone marrow, sorted "fraction B" pro-B cells or purified long-term reconstituting stem cells, contain significantly higher numbers of pro-B cells compared with controls, suggesting an inhibitory role for HK-1 on developing B cells. Supporting this idea, we show that addition of HK-1 to cultures established from long-term reconstituting stem cells and the newly described intermediate-term reconstituting stem cells leads to a significant decrease of de novo generated pro-B cells. Based on our studies, we postulate that HK-1 plays an inhibitory role in hematopoiesis, and we hypothesize that it may be part of the bone marrow microenvironment that supports and regulates the proliferation and differentiation of hematopoietic cells.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".