MétaCan
Menu
Back to cohort

Bone marrow cells from patients with Shwachman‐Diamond syndrome abnormally express genes involved in ribosome biogenesis and RNA processing

2009· article· en· W2023110459 on OpenAlexaff
Piya Rujkijyanont, Sally‐Lin Adams, Joseph Beyene, Yigal Dror

Bibliographic record

VenueBritish Journal of Haematology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBlood disorders and treatments
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsRibosome biogenesisGeneBiologyRNAGene expressionRibosomeDownregulation and upregulationRibosomal proteinMessenger RNATranscription (linguistics)Molecular biologyGenetics

Abstract

fetched live from OpenAlex

Shwachman-Diamond Syndrome (SDS) is a multi-system genetic disorder with bone marrow failure. SBDS, the gene associated with SDS, has been postulated to play a role in ribosome biogenesis and RNA processing, but its functions are still unknown. To study whether these pathways are interrupted when Sbds protein is lost, we studied the expression of related genes in patient SBDS-/- cells by an oligonucleotide microarray. We first analysed ribosomal protein (RP) genes, which are normally co-regulated. In SDS, 27 of the 85 RP genes were downregulated. Among the downregulated RP genes, seven are known to be associated with the inhibition of apoptosis. RPS27L, which mediates p53-dependent induction of apoptosis, was the only upregulated RP gene. Interestingly, several genes involved in RP mRNA transcription were downregulated without affecting the expression of genes involved in mRNA degradation, suggesting that the downregulation of the RP gene expression might be at the transcriptional level. Importantly we also found dysregulation of multiple genes involved in rRNA transcription and pre-rRNA processing. We conclude that SDS marrow cells exhibit major dysregulation of RP, RNA processing and RNA transcription genes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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 designObservational
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

Citations45
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of HaematologySame topicBlood disorders and treatmentsFrench-language works237,207