Control of osteocyte dendrite formation by Sp7 and its target gene osteocrin
Bibliographic record
Abstract
Abstract Osteocytes use an elaborate network of dendritic connections to control bone remodeling. Some osteoblasts embed within mineralized bone matrix, change shape, and become osteocytes. The molecular circuitry that drives dendrite formation during “osteocytogenesis” is poorly understood. Here we show that deletion of Sp7 , a gene linked to rare and common skeletal disease, in mature osteoblasts and osteocytes causes severe defects in osteocyte dendrites. Unbiased profiling of Sp7 target genes and binding sites reveals unexpected repurposing of this transcription factor to drive dendrite formation. Osteocrin is a Sp7 target gene that promotes osteocyte dendrite formation and rescues phenotypic and molecular defects in Sp7-deficient mice. Single-cell RNA-sequencing demonstrates overt defects in osteocyte maturation in vivo in the absence of Sp7. Sp7-dependent gene networks enriched in developing osteocytes are associated with rare and common human skeletal traits. Moreover, humans homozygous for the osteogenesis imperfecta-causing SP7 R316C mutation show dramatic defects in osteocyte morphology. Genes that mark osteocytes in vivo and that are regulated by Sp7 in vitro are highly enriched in neurons, highlighting shared features between osteocytic and neuronal connectivity. Taken together, these findings reveal a crucial role for Sp7 and its target gene Osteocrin in osteocytogenesis, demonstrating that pathways that control osteocyte development influence human bone diseases.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".