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Record W4379966501 · doi:10.1002/jbmr.4865

<scp>RNA</scp> Sequencing of <scp>Urine‐Derived</scp> Cells for the Characterization and Diagnosis of Osteogenesis Imperfecta

2023· article· en· W4379966501 on OpenAlexafffund
Karissa Ludwig, Zenghui Wu, Ghalib Bardai, Patrizia Mason, Leanne M. Ward, Pierre Moffatt, Frank Rauch

Bibliographic record

VenueJournal of Bone and Mineral Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of OttawaShriners Hospitals for Children - Canada
FundersShriners Hospitals for ChildrenUniversity of Ottawa
KeywordsRNA splicingIntronOsteogenesis imperfectaBiologyGeneGeneticsDNA sequencingRNAspliceExome sequencingAlternative splicingMolecular biologyMessenger RNAComputational biologyBioinformaticsMutation

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.333
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

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