Differential Effects of IL-15 and IL-21 in Myeloid (CD11b+) and Lymphoid (CD11b−) Bone Marrow Cells
Bibliographic record
Abstract
IL-15 has been found to activate NF-kappaB in various types of cells. However, the role of this transcription factor in IL-15- and IL-21-stimulated murine bone marrow (BM) cells is unclear. In this study, we demonstrated that both IL-15 and IL-21 are capable of delaying BM cell factor deprivation-induced apoptosis, but only IL-15 induced their proliferation. Following separation of BM cells into myeloid (CD11b(+)) and lymphoid (CD11b(-)) cell populations, we found that IL-15, but not IL-21, significantly induced proliferation in both cell populations. Both cytokines significantly delayed apoptosis, but only in CD11b(-) BM cells. IL-15Ralpha, CD122 (IL-2/15Rbeta), and common gamma-chains (CD132) were expressed in both populations, whereas IL-21Ralpha was expressed only in CD11b(-) BM cells. In addition, we demonstrated that IL-15-induced BM cell proliferation was significantly inhibited in NF-kappaBp50(-/-) mice when compared with littermate controls. The ability of IL-15 and IL-21 to delay BM cell apoptosis was slightly inhibited in NF-kappaBp50(-/-) mice, whereas the antiapoptotic effect of LPS was markedly reversed. We conclude that IL-15, but not IL-21, induces BM cell proliferation and that both cytokines delay BM cell apoptosis. These biological activities were preferentially observed in CD11b(-) BM cells. Using NF-kappaBp50(-/-) mice, we demonstrated for the first time that NF-kappaB plays a greater role in IL-15-induced cell proliferation than in IL-15- and IL-21-induced suppression of apoptosis.
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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".