Transcriptomic and lipidomic profiling provide novel insight into the pathogenesis of monogenic <i>SGMS2</i> -related osteoporosis
Bibliographic record
Abstract
Abstract Heterozygous pathogenic variants in the SGMS2 gene, encoding the sphingomyelin-synthesizing enzyme sphingomyelin synthase 2, cause a rare monogenic form of osteoporosis with low bone density, fractures, bone deformities, sclerotic cranial lesions, and occasionally, neurological symptoms. Three disease-causing heterozygous SGMS2 variants have been reported: c.148C>T (p.Arg50*), c.185T>G (p.Ile62Ser), and c.191T>G (p.Met64Arg). This study examined the cellular mechanisms of SGMS2-related osteoporosis and skeletal dysplasia through transcriptomic and lipidomic profiling of serum and fibroblasts from patients and controls. Bulk RNA sequencing and SCIEX lipidyzer-based lipidomics were employed. Differential expression analysis revealed 215 upregulated and 58 downregulated genes enriched in 169 Gene Ontology Biological Processes related to skeletal, neurological, ocular, muscular, and membrane functions. Pathway analysis revealed enriched pathways associated with interleukin signaling, electrical transmission across gap junctions, and circadian clock. Four lipid metabolism pathways were enriched: PPARα regulation, glycerophospholipid biosynthesis, phospholipid metabolism, and lipid metabolism. Lipidome analysis failed to detect significant differences between fibroblasts of patients and controls, while revealing 55 upregulated lipids, predominantly triacylglycerols (TAGs), but no downregulated lipids in serum of the patients. These findings suggest that SGMS2 variants modulate circadian rhythm and gap junction assembly, adversely affecting bone health and homeostasis, and may affect neuron-supporting cells in SGMS2-related osteoporosis.
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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 | 0.001 | 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".