<scp>RNA</scp> Sequencing of <scp>Urine‐Derived</scp> Cells for the Characterization and Diagnosis of Osteogenesis Imperfecta
Bibliographic record
Abstract
DNA sequencing is a reliable tool for identifying genetic variants in osteogenesis imperfecta (OI) but cannot always establish pathogenicity, particularly in variants altering splicing. RNA sequencing can provide functional evidence of the effect of a variant on the transcript but requires cells expressing the relevant genes. Here, we used urine-derived cells (UDC) to characterize genetic variants in patients with suspected or confirmed OI and provide evidence on the pathogenicity of variants of uncertain significance (VUS). Urine samples were obtained from 45 children and adolescents; UDC culture was successful in 40 of these participants (age range 4-20 years, 21 females), including 18 participants with OI or suspected OI who had a candidate variant or VUS on DNA sequencing. RNA was extracted from UDC and sequenced on an Illumina NextSeq550 device. Principal component analysis showed that the gene expression profiles of UDC and fibroblasts (based on Genotype Tissue Expression [GTEx] Consortium data) clustered close together and had less variability than those of whole blood cells. Transcript abundance was sufficient for analysis by RNA sequencing (defined as a median gene expression level of ≥10 transcripts per million) for 25 of the 32 bone fragility genes (78%) that were included in our diagnostic DNA sequencing panel. These results were similar to GTEx data for fibroblasts. Abnormal splicing was identified in 7 of the 8 participants with pathogenic or likely pathogenic variants in the splice region or deeper within the intron. Abnormal splicing was also observed in 2 VUS (COL1A1 c.2829+5G>A and COL1A2 c.693+6T>G), but no splice abnormality was observed in 3 other VUS. Abnormal deletions and duplications could also be observed in UDC transcripts. In conclusion, UDC are suitable for RNA transcript analysis in patients with suspected OI and can provide functional evidence for pathogenicity, in particular of variants affecting splicing. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.002 | 0.003 |
| 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".