Nfil3, a target of the NACA transcriptional coregulator, affects osteoblast and osteocyte gene expression differentially
Bibliographic record
Abstract
Intermittent administration of PTH(1-34) has a profound osteoanabolic effect on the skeleton. At the cellular level, osteoblasts and osteocytes are two crucial cell types that respond to PTH stimulation in bone. The transcriptional cofactor Nascent polypeptide Associated Complex and coregulator alpha (NACA) is a downstream target of the PTH-Gαs-PKA axis in osteoblasts. NACA functions as a transcriptional cofactor affecting bZIP factor-mediated transcription of target promoters in osteoblasts, such as Osteocalcin (Bglap2). Here, we used RNA-Seq and ChIP-Seq against NACA in PTH-treated MC3T3-E1 osteoblastic cells to identify novel targets of the PTH-activated NACA. Our approach identified Nuclear factor interleukin-3-regulated (Nfil3) as a target promoter of this pathway. Knockdown of Naca reduced the response of Nfil3 to PTH(1-34) stimulation. In silico analysis of the Nfil3 promoter revealed potential binding sites for NACA (located within the ChIP fragment) and CREB. We show that following PTH stimulation, phosphorylated-CREB binds the proximal promoter of Nfil3 in osteoblasts. The activity of the Nfil3 promoter (-818/+182 bp) is regulated by CREB and this activation relies on the presence of NACA. In addition, we show that knockdown of Nfil3 enhances the expression of osteoblastic differentiation markers in MC3T3-E1 cells while it represses osteocytic marker gene expression in IDG-SW3 cells. These results show that the PTH-induced NACA axis regulates Nfil3 expression and suggest that NFIL3 acts as a transcriptional repressor in osteoblasts while it exhibits differential activity as an activator in osteocytes.
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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".