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Record W2163597140 · doi:10.4161/rdis.29515

FUS-regulated RNA metabolism and DNA damage repair

2014· article· en· W2163597140 on OpenAlexafffund
Yueqin Zhou, Songyan Liu, Arzu Öztürk, Geoffrey G. Hicks

Bibliographic record

VenueRare Diseases · 2014
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of ManitobaManitoba Beekeepers' Association
FundersCanada Research ChairsManitoba Health Research Council
KeywordsNeurodegenerationRNA splicingBiologyRNAC9orf72RNA-binding proteinIntronAmyotrophic lateral sclerosisDNA damageAlternative splicingCell biologyGene expressionFrontotemporal dementiaGeneTARDBPMessenger RNAGeneticsDNAMutantTrinucleotide repeat expansionSOD1MedicinePathologyAllele

Abstract

fetched live from OpenAlex

Cytoplasmic inclusion of RNA binding protein FUS/TLS in neurons and glial cells is a characteristic pathology of a subgroup of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Dysregulation of RNA metabolism caused by FUS cytoplasmic inclusion emerges to be a key event in FUS-associated ALS/FTD pathogenesis. Our recent discovery of a FUS autoregulatory mechanism and its dysregulation in ALS-FUS mutants demonstrated that dysregulated alternative splicing can directly exacerbate the pathological FUS accumulation. We show here that FUS targets RNA for pre-mRNA alternative splicing and for the processing of long intron-containing transcripts, and that these targets are enriched for genes in neurogenesis and gene expression regulation. We also identify that FUS RNA targets are enriched for genes in the DNA damage response pathway. Together, the data support a model in which dysregulated RNA metabolism and DNA damage repair together may render neurons more vulnerable and accelerate neurodegeneration in ALS and FTD.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.269
Teacher spread0.255 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

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