Integrating glycomics and genomics uncovers SLC10A7 as essential factor for bone mineralization by regulating post-Golgi protein transport and glycosylation
Bibliographic record
Abstract
Genomics methodologies have significantly improved elucidation of Mendelian disorders. The combination with high-throughput functional-omics technologies potentiates the identification and confirmation of causative genetic variants, especially in singleton families of recessive inheritance. In a cohort of 99 individuals with abnormal Golgi glycosylation, 47 of which being unsolved, glycomics profiling was performed of total plasma glycoproteins. Combination with whole-exome sequencing in 31 cases revealed a known genetic defect in 15 individuals. To identify additional genetic factors, hierarchical clustering of the plasma glycomics data was done, which indicated a subgroup of four patients that shared a unique glycomics signature of hybrid type N-glycans. In two siblings, compound heterozygous mutations were found in SLC10A7, a gene of unknown function in human. These included a missense mutation that disrupted transmembrane domain 4 and a mutation in a splice acceptor site resulting in skipping of exon 9. The two other individuals showed a complete loss of SLC10A7 mRNA. The patients' phenotype consisted of amelogenesis imperfecta, skeletal dysplasia, and decreased bone mineral density compatible with osteoporosis. The patients' phenotype was mirrored in SLC10A7 deficient zebrafish. Furthermore, alizarin red staining of calcium deposits in zebrafish morphants showed a strong reduction in bone mineralization. Cell biology studies in fibroblasts of affected individuals showed intracellular mislocalization of glycoproteins and a defect in post-Golgi transport of glycoproteins to the cell membrane. In contrast to yeast, human SLC10A7 localized to the Golgi. Our combined data indicate an important role for SLC10A7 in bone mineralization and transport of glycoproteins to the extracellular matrix.
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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 | 0.000 |
| 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) | 0.000 | 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".