Targeting 3D chromosomal architecture at the RANK loci to suppress myeloma-driven osteoclastogenesis
Bibliographic record
Abstract
Bone disease represents a major cause of morbidity and mortality in Multiple Myeloma (MM); primarily driven by osteoclasts whose differentiation is dependent on expression of RANKL by MM cells. Notably, costimulation by ITAM containing receptors (i.e., FcγR) can also play a crucial role in osteoclast differentiation. Modeling the pathology of the bone marrow microenvironment with an ex vivo culture system of primary human multiple myeloma cells, we herein demonstrate that FcγR-mediated signaling, via staphylococcal protein A (SpA) IgG immune-complexes, can act as a critical negative regulator of MM-driven osteoclast differentiation. Interrogation of the mode-of-action revealed that FcγR-mediated signaling causes epigenetic modulation of chromosomal 3D architecture at the RANK promoter; with altered spatial orientation of a proximal super enhancer. Combined this leads to substantial down-regulation of RANK at a transcript, protein, and functional level. These observations shed light on a novel mechanism regulating RANK expression and provide a rationale for targeting FcγR-signaling for the amelioration of osteolytic bone pathology in disease.
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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".