Inhibition of Osteoclast Bone Resorption by Disrupting Vacuolar H+-ATPase a3-B2 Subunit Interaction
Bibliographic record
Abstract
Vacuolar H(+)-ATPases (V-ATPases) are highly expressed in ruffled borders of bone-resorbing osteoclasts, where they play a crucial role in skeletal remodeling. To discover protein-protein interactions with the a subunit in mammalian V-ATPases, a GAL4 activation domain fusion library was constructed from an in vitro osteoclast model, receptor activator of NF-κB ligand-differentiated RAW 264.7 cells. This library was screened with a bait construct consisting of a GAL4 binding domain fused to the N-terminal domain of V-ATPase a3 subunit (NTa3), the a subunit isoform that is highly expressed in osteoclasts (a1 and a2 are also expressed, to a lesser degree, whereas a4 is kidney-specific). One of the prey proteins identified was the V-ATPase B2 subunit, which is also highly expressed in osteoclasts (B1 is not expressed). Further characterization, using pulldown and solid-phase binding assays, revealed an interaction between NTa3 and the C-terminal domains of both B1 and B2 subunits. Dual B binding domains of equal affinity were observed in NTa, suggesting a possible model for interaction between these subunits in the V-ATPase complex. Furthermore, the a3-B2 interaction appeared to be moderately favored over a1, a2, and a4 interactions with B2, suggesting a mechanism for the specific subunit assembly of plasma membrane V-ATPase in osteoclasts. Solid-phase binding assays were subsequently used to screen a chemical library for inhibitors of the a3-B2 interaction. A small molecule benzohydrazide derivative was found to inhibit osteoclast resorption with an IC(50) of ∼1.2 μm on both synthetic hydroxyapatite surfaces and dentin slices, without significantly affecting RAW 264.7 cell viability or receptor activator of NF-κB ligand-mediated osteoclast differentiation. Further understanding of these interactions and inhibitors may contribute to the design of novel therapeutics for bone loss disorders, such as osteoporosis and rheumatoid arthritis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".