Characterization of Gaucher disease bone marrow mesenchymal stromal cells reveals an altered inflammatory secretome
Bibliographic record
Abstract
Gaucher disease causes pathologic skeletal changes that are not fully explained. Considering the important role of mesenchymal stromal cells (MSCs) in bone structural development and maintenance, we analyzed the cellular biochemistry of MSCs from an adult patient with Gaucher disease type 1 (N370S/L444P mutations). Gaucher MSCs possessed a low glucocerebrosidase activity and consequently had a 3-fold increase in cellular glucosylceramide. Gaucher MSCs have a typical MSC marker phenotype, normal osteocytic and adipocytic differentiation, growth, exogenous lactosylceramide trafficking, cholesterol content, lysosomal morphology, and total lysosomal content, and a marked increase in COX-2, prostaglandin E2, interleukin-8, and CCL2 production compared with normal controls. Transcriptome analysis on normal MSCs treated with the glucocerebrosidase inhibitor conduritol B epoxide showed an up-regulation of an array of inflammatory mediators, including CCL2, and other differentially regulated pathways. These cells also showed a decrease in sphingosine-1-phosphate. In conclusion, Gaucher disease MSCs display an altered secretome that could contribute to skeletal disease and immune disease manifestations in a manner distinct and additive to Gaucher macrophages themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".