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Record W4386818355 · doi:10.1093/brain/awad315

Clinical and genetic characterisation of a large Indian congenital myasthenic syndrome cohort

2023· article· en· W4386818355 on OpenAlexafffund
Kiran Polavarapu, B. Sunitha, Ana Töpf, Veeramani Preethish‐Kumar, Rachel Thompson, Seena Vengalil, Saraswati Nashi, Mainak Bardhan, Sai Bhargava Sanka, Akshata Huddar, Gopikrishnan Unnikrishnan, Gautham Arunachal, Manu Santhappan Girija, Anna Porter, Yoshiteru Azuma, Paulo José Lorenzoni, Dipti Baskar, Ram Murthy Anjanappa, Madassu Keertipriya, Hansashree Padmanabh, Ganaraja Valakunja Harikrishna, Steven Laurie, Leslie Matalonga, Rita Horváth, Atchayaram Nalini, Hanns Lochmüller

Bibliographic record

VenueBrain · 2023
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity of OttawaOttawa HospitalChildren's Hospital of Eastern Ontario
FundersNational Eye InstituteNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchCanada First Research Excellence FundCanada Research ChairsGovernment of CanadaMuscular Dystrophy CanadaNewton FundCanada Foundation for InnovationEuropean CommissionBroad Institute
KeywordsCohortCongenital myasthenic syndromeMedicineExome sequencingGenetic testingGeneticsInternal medicinePediatricsPhenotypeGeneBiologyMutation

Abstract

fetched live from OpenAlex

Congenital myasthenic syndromes (CMS) are a rare group of inherited disorders caused by gene defects associated with the neuromuscular junction and potentially treatable with commonly available medications such as acetylcholinesterase inhibitors and β2 adrenergic receptor agonists. In this study, we identified and genetically characterized the largest cohort of CMS patients from India to date. Genetic testing of clinically suspected patients evaluated in a South Indian hospital during the period 2014-19 was carried out by standard diagnostic gene panel testing or using a two-step method that included hotspot screening followed by whole-exome sequencing. In total, 156 genetically diagnosed patients (141 families) were characterized and the mutational spectrum and genotype-phenotype correlation described. Overall, 87 males and 69 females were evaluated, with the age of onset ranging from congenital to fourth decade (mean 6.6 ± 9.8 years). The mean age at diagnosis was 19 ± 12.8 (1-56 years), with a mean diagnostic delay of 12.5 ± 9.9 (0-49 years). Disease-causing variants in 17 CMS-associated genes were identified in 132 families (93.6%), while in nine families (6.4%), variants in genes not associated with CMS were found. Overall, postsynaptic defects were most common (62.4%), followed by glycosylation defects (21.3%), synaptic basal lamina genes (4.3%) and presynaptic defects (2.8%). Other genes found to cause neuromuscular junction defects (DES, TEFM) in our cohort accounted for 2.8%. Among the individual CMS genes, the most commonly affected gene was CHRNE (39.4%), followed by DOK7 (14.4%), DPAGT1 (9.8%), GFPT1 (7.6%), MUSK (6.1%), GMPPB (5.3%) and COLQ (4.5%). We identified 22 recurrent variants in this study, out of which eight were found to be geographically specific to the Indian subcontinent. Apart from the known common CHRNE variants p.E443Kfs*64 (11.4%) and DOK7 p.A378Sfs*30 (9.3%), we identified seven novel recurrent variants specific to this cohort, including DPAGT1 p.T380I and DES c.1023+5G>A, for which founder haplotypes are suspected. This study highlights the geographic differences in the frequencies of various causative CMS genes and underlines the increasing significance of glycosylation genes (DPAGT1, GFPT1 and GMPPB) as a cause of neuromuscular junction defects. Myopathy and muscular dystrophy genes such as GMPPB and DES, presenting as gradually progressive limb girdle CMS, expand the phenotypic spectrum. The novel genes MACF1 and TEFM identified in this cohort add to the expanding list of genes with new mechanisms causing neuromuscular junction defects.

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 score0.008
Threshold uncertainty score0.304

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 head0.018
GPT teacher head0.310
Teacher spread0.293 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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