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Record W4406833099 · doi:10.1016/j.nbd.2025.106814

Mutations in hnRNP A1 drive neurodegeneration and alternative RNA splicing of neuronal gene targets

2025· article· en· W4406833099 on OpenAlexafffund
Patricia A. Thibault, Hannah E. Salapa, Joseph-Patrick W. E. Clarke, Michael C. Levin

Bibliographic record

VenueNeurobiology of Disease · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Health Research FoundationSaskatoon City Hospital FoundationUniversity of Saskatchewan
KeywordsNeurodegenerationRNA splicingBiologyGeneRNAAlternative splicingGeneticsRna processingNeuroscienceMessenger RNAMedicineDisease

Abstract

fetched live from OpenAlex

RNA binding protein dysfunction is a pathogenic feature of multiple neurological diseases, including multiple sclerosis (MS). Neurodegeneration (the loss of, or damage to neurons and axons) is the primary driver of disease progression in MS. Herein, we utilized a novel, neuron-specific model of neurodegeneration by transducing primary mouse neurons with mutant forms of the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) identified from MS patients, including one within the M9-nuclear localization sequence of hnRNP A1 (A1(P275S)) and a second in the prion-like domain of hnRNP A1 (A1(F263S)) to test the hypothesis that neuronal hnRNP A1 dysfunction drives neurodegeneration in MS. Examination of hnRNP A1 localization in neurons revealed an increase in nucleocytoplasmic mislocalization in neurons transduced with A1(P275S), but not A1(F263S). Yet, both A1(F263S) and A1(P275S) induced neurodegeneration evidenced by significant reductions in total neurite length and complexity and an increase in FluoroJade-C neuronal cell body staining. RNA sequencing and differential alternative splicing analysis of mutant-expressing neurons revealed dramatic changes in alternative RNA splicing of transcripts critical to neuronal function. Further, amyloid precursor protein ( APP ), a marker for neurodegeneration in MS, showed differential splicing in mutant-expressing neurons, which was confirmed in MS brains with hnRNP A1 dysfunction. Overall, we have identified that hnRNP A1 plays a complex role in neuronal function and regulation by mediating the alternative splicing of neuron-specific transcripts. When neuronal hnRNP A1 function is impaired, as in disease, resultant dysfunction propagates through multiple pathways that may influence the progression of neurodegeneration in MS. • RNA binding protein dysfunction is a feature of neurodegeneration in neurologic disease, including multiple sclerosis. • Somatic mutations in the RBP hnRNP A1 were identified from MS patients but their impact on neurodegeneration is unclear. • Neuronal expression of two MS-derived mutations in hnRNP A1 alter splicing of neuronal mRNAs leading to neurodegeneration. • Dysfunctional RNA binding proteins, including hnRNP A1, can alter splicing and induce neurodegeneration in neurologic disease.

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 score0.002

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 integrity0.0000.001
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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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