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Record W4409468606 · doi:10.1016/j.isci.2025.112430

Multiple sclerosis severity variant in DYSF-ZNF638 locus associates with neuronal loss and inflammation

2025· article· en· W4409468606 on OpenAlexaff
Hendrik J. Engelenburg, Aletta M.R. van den Bosch, J.Q. Alida Chen, Cheng‐Chih Hsiao, Marie‐José Melief, Adil Harroud, Inge Huitinga, Jörg Hamann, Joost Smolders

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersStichting MS Research
KeywordsMultiple sclerosisInflammationLocus (genetics)NeuroscienceBiologyGeneticsMedicineImmunologyGene

Abstract

fetched live from OpenAlex

The genetic variant rs10191329 AA has been identified to associate with faster disability accrual in multiple sclerosis (MS). We investigated the impact of rs10191329 AA carriership on MS pathology and flanking genes dysferlin ( DYSF ) and zinc finger protein 638 ( ZNF638 ) in the Netherlands Brain Bank cohort ( n = 290) by comparing rs10191329 AA ( n = 6) to matched rs10191329 CC carriers ( n = 12). rs10191329 AA carriership associated with more acute axonal stress, reduced layer 2 neuronal density, and a higher proportion of lesions with foamy microglia. In rs10191329 AA donors, normal appearing white matter was characterized by a higher proportion of ZNF638 + oligodendrocytes, and normal appearing gray matter showed more DYSF + cells. Nuclear RNA sequencing showed an upregulation of mitochondrial genes in rs10191329 AA carriers. These data suggest that MS severity associates with an increased susceptibility to neurodegeneration and chronic inflammation. Understanding the role of DYSF, ZNF638, and mitochondrial pathways may reveal new therapeutic targets to attenuate MS progression.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.274

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 head0.011
GPT teacher head0.220
Teacher spread0.208 · 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

Citations16
Published2025
Admission routes1
Has abstractyes

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