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Record W4301595356 · doi:10.1007/s00401-022-02503-7

Assessment of PABPN1 nuclear inclusions on a large cohort of patients and in a human xenograft model of oculopharyngeal muscular dystrophy

2022· article· en· W4301595356 on OpenAlexaff
Fanny Roth, Jamila Dhiab, Alexis Boulinguiez, Hadidja‐Rose Mouigni, Saskia Lassche, Elisa Négroni, Laura Muraine, Alix Marhic, Alison Oliver, Jeanne Lainé, Andrée Rouche, Erin O’Ferrall, Baziel G.M. van Engelen, Coen A. C. Ottenheijm, Hagar Greif, Sergiu C. Blumen, Jean Lacau St Guily, Sophie Périé, Gillian Butler‐Browne, Vincent Mouly, Capucine Trollet

Bibliographic record

VenueActa Neuropathologica · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institutes of HealthStichting Spieren voor SpierenAFM-TéléthonAgence Nationale de la RechercheSorbonne UniversitéPrinses Beatrix SpierfondsFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche MédicaleFondation Maladies Rares
KeywordsOculopharyngeal muscular dystrophyMuscle biopsyMuscular dystrophyBiologyOrganelleChaperone (clinical)BiopsyPathologyCell biologyChemistryMedicineGenetics

Abstract

fetched live from OpenAlex

Oculopharyngeal muscular dystrophy (OPMD) is a rare muscle disease characterized by an onset of weakness in the pharyngeal and eyelid muscles. The disease is caused by the extension of a polyalanine tract in the Poly(A) Binding Protein Nuclear 1 (PABPN1) protein leading to the formation of intranuclear inclusions or aggregates in the muscle of OPMD patients. Despite numerous studies stressing the deleterious role of nuclear inclusions in cellular and animal OPMD models, their exact contribution to human disease is still unclear. In this study, we used a large and unique collection of human muscle biopsy samples to perform an in-depth analysis of PABPN1 aggregates in relation to age, genotype and muscle status with the final aim to improve our understanding of OPMD physiopathology. Here we demonstrate that age and genotype influence PABPN1 aggregates: the percentage of myonuclei containing PABPN1 aggregates increases with age and the chaperone HSP70 co-localize more frequently with PABPN1 aggregates with a larger polyalanine tract. In addition to the previously described PRMT1 and HSP70 co-factors, we identified new components of PABPN1 aggregates including GRP78/BiP, RPL24 and p62. We also observed that myonuclei containing aggregates are larger than myonuclei without. When comparing two muscles from the same patient, a similar amount of aggregates is observed in different muscles, except for the pharyngeal muscle where fewer aggregates are observed. This could be due to the peculiar nature of this muscle which has a low level of PAPBN1 and contains regenerating fibers. To confirm the fate of PABPN1 aggregates in a regenerating muscle, we generated a xenograft model by transplanting human OPMD muscle biopsy samples into the hindlimb of an immunodeficient mouse. Xenografts from subjects with OPMD displayed regeneration of human myofibers and PABPN1 aggregates were rapidly present-although to a lower extent-after muscle fiber regeneration. Our data obtained on human OPMD samples add support to the dual non-exclusive models in OPMD combining toxic PABPN1 intranuclear inclusions together with PABPN1 loss of function which altogether result in this late-onset and muscle selective 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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.418

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 science0.0000.001
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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 designObservational
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

Citations16
Published2022
Admission routes1
Has abstractyes

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