Autocrine Regulation of Interferon <i>γ</i> in Mesenchymal Stem Cells Plays a Role in Early Osteoblastogenesis
Bibliographic record
Abstract
Interferon (IFN)gamma is a strong inhibitor of osteoclast differentiation and activity. However, its role in osteoblastogenesis has not been carefully examined. Using microarray expression analysis, we found that several IFNgamma-inducible genes were upregulated during early phases of osteoblast differentiation of human mesenchymal stem cells (hMSCs). We therefore hypothesized that IFNgamma may play a role in this process. We first observed a strong and transient increase in IFNgamma production following hMSC induction to differentiate into osteoblasts. We next blocked this endogenous production using a knockdown approach with small interfering RNA and observed a strong inhibition of hMSC differentiation into osteoblasts with a concomitant decrease in Runx2, a factor indispensable for osteoblast development. Additionally, exogenous addition of IFNgamma accelerated hMSC differentiation into osteoblasts in a dose-dependent manner and induced higher levels of Runx2 expression during the early phase of differentiation. We next examined IFNgamma signaling in vivo in IFNgamma receptor 1 knockout (IFNgammaR1(-/-)) mice. Compared with their wild-type littermates, IFNgammaR1(-/-) mice exhibited a reduction in bone mineral density. As in the in vitro experiments, MSCs obtained from IFNgammaR1(-/-) mice showed a lower capacity to differentiate into osteoblasts. In summary, we demonstrate that the presence of IFNgamma plays an important role during the commitment of MSCs into the osteoblastic lineage both in vitro and in vivo, and that this process can be accelerated by exogenous addition of IFNgamma. These data therefore support a new role for IFNgamma as an autocrine regulator of hMSC differentiation and as a potential new target of bone-forming cells in vivo.
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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".