MétaCan
Menu
← Back to cohort
Record W6976300135 · doi:10.60692/s0r0y-kjv31

POLRMT mutations impair mitochondrial transcription causing neurological disease

2021· article· en· W6976300135 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsMitochondrial DNATranscription (linguistics)Mitochondrial diseaseHuman mitochondrial geneticsPolymeraseMitochondrionRNA polymerase IIPhenotypeGeneMutation

Abstract

fetched live from OpenAlex

Abstract While >300 disease-causing variants have been identified in the mitochondrial DNA (mtDNA) polymerase γ, no mitochondrial phenotypes have been associated with POLRMT, the RNA polymerase responsible for transcription of the mitochondrial genome. Here, we characterise the clinical and molecular nature of POLRMT variants in eight individuals from seven unrelated families. Patients present with global developmental delay, hypotonia, short stature, and speech/intellectual disability in childhood; one subject displayed an indolent progressive external ophthalmoplegia phenotype. Massive parallel sequencing of all subjects identifies recessive and dominant variants in the POLRMT gene. Patient fibroblasts have a defect in mitochondrial mRNA synthesis, but no mtDNA deletions or copy number abnormalities. The in vitro characterisation of the recombinant POLRMT mutants reveals variable, but deleterious effects on mitochondrial transcription. Together, our in vivo and in vitro functional studies of POLRMT variants establish defective mitochondrial transcription as an important disease mechanism.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0030.000

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 head0.024
GPT teacher head0.220
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGreater South Information System→Same topicMitochondrial Function and Pathology→French-language works237,207→