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Record W4385186850 · doi:10.1016/j.ebiom.2023.104720

Widespread dysregulation of mRNA splicing implicates RNA processing in the development and progression of Huntington's disease

2023· article· en· W4385186850 on OpenAlexafffund
Vincent Tano, Kagistia Hana Utami, Nur Amirah Binte Mohammad Yusof, Jocelyn Bégin, Willy Wei Li Tan, Mahmoud A. Pouladi, Sarah R. Langley

Bibliographic record

VenueEBioMedicine · 2023
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Supercomputing Centre SingaporeNanyang Technological UniversityLee Kong Chian School of Medicine, Nanyang Technological UniversityMinistry of Education - SingaporeBC Children's HospitalMichael Smith Health Research BCYale-NUS CollegeYale University
KeywordsRNA splicingBiologyAlternative splicingHuntingtinHuntington's diseaseMinigeneRNA-binding proteinEpigeneticsGeneticsGeneMessenger RNARNAMutantDiseaseMedicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: In Huntington's disease (HD), a CAG repeat expansion mutation in the Huntingtin (HTT) gene drives a gain-of-function toxicity that disrupts mRNA processing. Although dysregulation of gene splicing has been shown in human HD post-mortem brain tissue, post-mortem analyses are likely confounded by cell type composition changes in late-stage HD, limiting the ability to identify dysregulation related to early pathogenesis. METHODS: To investigate gene splicing changes in early HD, we performed alternative splicing analyses coupled with a proteogenomics approach to identify early CAG length-associated splicing changes in an established isogenic HD cell model. FINDINGS: We report widespread neuronal differentiation stage- and CAG length-dependent splicing changes, and find an enrichment of RNA processing, neuronal function, and epigenetic modification-related genes with mutant HTT-associated splicing. When integrated with a proteomics dataset, we identified several of these differential splicing events at the protein level. By comparing with human post-mortem and mouse model data, we identified common patterns of altered splicing from embryonic stem cells through to post-mortem striatal tissue. INTERPRETATION: We show that widespread splicing dysregulation in HD occurs in an early cell model of neuronal development. Importantly, we observe HD-associated splicing changes in our HD cell model that were also identified in human HD striatum and mouse model HD striatum, suggesting that splicing-associated pathogenesis possibly occurs early in neuronal development and persists to later stages of disease. Together, our results highlight splicing dysregulation in HD which may lead to disrupted neuronal function and neuropathology. FUNDING: This research is supported by the Lee Kong Chian School of Medicine, Nanyang Technological University Singapore Nanyang Assistant Professorship Start-Up Grant, the Singapore Ministry of Education under its Singapore Ministry of Education Academic Research Fund Tier 1 (RG23/22), the BC Children's Hospital Research Institute Investigator Grant Award (IGAP), and a Scholar Award from the Michael Smith Health Research BC.

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)0.001
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 score0.362
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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

Citations23
Published2023
Admission routes2
Has abstractyes

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