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Record W4407552855 · doi:10.1186/s40478-025-01946-9

Blood biomarker fingerprints in a cohort of patients with CHRNE-related congenital myasthenic syndrome

2025· article· en· W4407552855 on OpenAlexafffund
Adela Della Marina, Andrie Koutsoulidou, Daniel Natera‐de Benito, Lars‐Oliver Tykocinski, Marios Tomazou, Kristia Georgiou, Andreas Laner, Heike Kölbel, A. Nascimento, C. Ortez, Angela Abicht, Basant Kumar Thakur, Hanns Lochmüller, Leonidas A. Phylactou, Tobias Ruck, Ulrike Schara‐Schmidt, D. D. Kale, Andreas Hentschel, Andreas Roos

Bibliographic record

VenueActa Neuropathologica Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersMinisterium für Kultur und Wissenschaft des Landes Nordrhein-WestfalenInstituto de Salud Carlos IIICanadian Institutes of Health ResearchDeutsche Gesellschaft für MuskelkrankeUniversitätsklinikum EssenBundesministerium für Bildung und ForschungStiftung Universitätsmedizin EssenEuropean Regional Development FundEuropean CommissionCanada First Research Excellence FundCanada Research ChairsGovernment of Canada
KeywordsBiomarkerProteomicsInternal medicineMedicineBiologyImmunologyBiochemistryGene

Abstract

fetched live from OpenAlex

Mutations in CHRNE encoding the epsilon subunit of acetylcholine receptor result in impaired neuromuscular transmission and congenital myasthenic syndrome (CMS) with variying severity of symptoms. Although the pathophysiology is well-known, blood biomarker signatures enabling a patient-stratification are lacking. This retrospective two-center-study includes 19 recessive CHRNE-patients (AChR deficiency; mean age 14.8 years) from 13 families which were clinically characterized according to disease severity. 15 patients were classified as mildly and 4 patients as moderate to severely affected. Seven known pathogenic and one unreported variant (c.1032 + 2_1032 + 3delinsGT) were identified. Biomarker discovery was carried out on blood samples: proteomics was performed on white blood cells (WBC; n = 12) and on extracellular vesicles (EV) purified from serum samples (n = 7) in addition to amino acid profiling (n = 9) and miRNA screening (n = 18). For miRNA studies, 7 patients with other CMS-subtypes were moreover included. WBC-proteomics unveiled a significant increase of 7 and a decrease of 36 proteins. In silico studies of these proteins indicated affection of secretory granules and the extracellular space. Comparison across patients unveiled increase of two vesicular transport proteins (SCAMP2 and SNX2) in severely affected patients and indeed EV-proteomics revealed increase of 7 and decrease of 13 proteins. Three of these proteins (TARSH, ATRN & PLEC) are known to be important for synaptogenesis and synaptic function. Metabolomics showed decrease of seven amino acids/ amino acid metabolites (aspartic and glutamic acids, phosphoserine, amino adipate, citrulline, ornithine, and 1-methyhistidine). miRNA-profiling showed increase miR - 483 - 3p, miR-365a-3p, miR - 365b - 3p and miR-99a, and decrease of miR-4433b-3p, miR-6873-3p, miR-182-5p and let-7b-5p in CHRNE-patients whereas a comparison with other CMS subtypes showed increase of miR - 205 - 5p, miR - 10b - 5p, miR-125a-5p, miR-499-5p, miR-3120-5p and miR - 483 - 5p and decrease of miR - 1290. Our combined data introduce a molecular fingerprint on protein, metabolic and miRNA level with some of those playing different roles along the neuromuscular axis.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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