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Record W3122883280 · doi:10.1016/j.celrep.2020.108634

Transcriptome alterations in myotonic dystrophy frontal cortex

2021· article· en· W3122883280 on OpenAlexfundno aff
Brittney A. Otero, Kiril Poukalov, Ryan P. Hildebrandt, Charles A. Thornton, Kenji Jinnai, Harutoshi Fujimura, Takashi Kimura, Katharine A. Hagerman, Jacinda B. Sampson, Eric T. Wang

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersInstitute of GeneticsMcKnight FoundationNational Institute of Neurological Disorders and StrokeUniversity of FloridaChan Zuckerberg InitiativeMuscular Dystrophy Association
KeywordsMyotonic dystrophyBiologyAlternative splicingTranscriptomeRNA splicingExonNeuroscienceGeneticsGeneGene expressionRNA

Abstract

fetched live from OpenAlex

Myotonic dystrophy (DM) is caused by expanded CTG/CCTG repeats, causing symptoms in skeletal muscle, heart, and central nervous system (CNS). CNS issues are debilitating and include hypersomnolence, executive dysfunction, white matter atrophy, and neurofibrillary tangles. Here, we generate RNA-seq transcriptomes from DM and unaffected frontal cortex and identify 130 high-confidence splicing changes, most occurring only in cortex, not skeletal muscle or heart. Mis-spliced exons occur in neurotransmitter receptors, ion channels, and synaptic scaffolds, and GRIP1 mis-splicing modulates kinesin association. Optical mapping of expanded CTG repeats reveals extreme mosaicism, with some alleles showing >1,000 CTGs. Mis-splicing severity correlates with CTG repeat length across individuals. Upregulated genes tend to be microglial and endothelial, suggesting neuroinflammation, and downregulated genes tend to be neuronal. Many gene expression changes strongly correlate with mis-splicing, suggesting candidate biomarkers of disease. These findings provide a framework for mechanistic and therapeutic studies of the DM CNS.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.647

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations59
Published2021
Admission routes1
Has abstractyes

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