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Enregistrement W4402451781 · doi:10.3389/fgene.2024.1481705

Editorial: Diagnosis, animal models and therapeutic interventions for neuromuscular diseases

2024· editorial· en· W4402451781 sur OpenAlexaff
Muthukumar Karuppasamy, Martine Tétreault, Jessica Rosati

Notice bibliographique

RevueFrontiers in Genetics · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueNeurogenetic and Muscular Disorders Research
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésMedicinePsychological interventionIntensive care medicineBioinformaticsBiologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Neuromuscular disorders (NMDs) are a group of rare diseases that largely affect the muscles, peripheral nervous system and neuromuscular junctions. Several genetic variations are associated with various types of NMDs. The fatal consequences of NMDs include skeletal muscle waste, reduction in muscle strength, muscle degeneration, neuropathy, spinal cord and neuromuscular junction abnormalities, sensory and motor impairment, lack of mobility and/or defects in heart and lungs [1][2][3] . The clinical diagnosis and the clinical management of NMDs in children and adults is very challenging since the majority of NMDs patients present unspecific clinical and pathological features that are similar between NMD sutypes 4,5 . The current diagnostic approaches include genetic testing, biochemical evaluation, muscle and nerve biopsy, computerized tomography, electromyography and magnetic resonance imaging [6][7][8][9] .Technological progress has greatly improved our ability to diagnosed patients affected with NMDs; however, the majority are still very limited treatment options 10 . As an example, Duchenne muscular dystrophy patients have been treated with corticosteroids which exhibit detrimental adverse effects such as weight gain, behavioral problems and fragile bones. As for modeling of diseases, there are currently no efficient animal models to recapitulate key phenotypes seen in most NMDs patients. Absence of animal models further hinders the drug development for NMDs. Therefore, therapeutic strategies aimed at curing these NMDs efficiently are warranted now. The drug discovery and development approaches including gene editing, drug repurposing, RNA-based intervention and gene replacement therapy have greatly helped toward clinical trials and other drug developmental phases conducted for various NMDs. In the current scenario, creating novel animal models, gene therapy and biomarkers for NMDs have been on the rise as it may be beneficial in understanding disease pathology, evaluating variant-specific therapeutic interventions and advancing them into patient use. The key goal of this research topic is to report novel diagnosis, therapeutic interventions, animal models, disease management in NMDs. This topic is also aimed at incorporating the newer diagnostic approach or genetic screening method for any NMDs. Multiple manuscripts were submitted for the publication of this research topic and a few articles were accepted and published.Tang et al. presented a case carrying a variant in the SCP2 gene (OMIM number: 184755), known for its involvement in the process of peroxisomal beta oxidation. To date, only two cases of SCPx deficiency have been reported. This patient has symmetrical lesions of the thalamus and brainstem, as previously described, but this variant causes a notably different clinical phenotype characterized by episodic psychosis. It may be suggested that homozygosity for a rare variant brings to question potential for consanguinity and other potential causes of an unusual phenotype. The authors identified a homozygous splicing variant resulting in skipping of exon 8 and the consequent loss of 29 amino acids in the SCP2 protein. In another study, Swindell conducted a metaanalysis of six lower motor neuron gene expression studies obtained by laser-capture microdissection of post-mortem spinal cord from ALS and control subjects. Of note, the six datasets included 56 ALS patients (with both sporadic and genetic ALS such as C9ORF72, SOD1 and CHMP2B) and 37 healthy donors. This study identified 500 differentially expressed genes (DEGs) among ALS and CTL samples highlighting 10 key functional categories implicated in the disease. The analysis also connected ALS transcriptomics to genetics, first by demonstrating overlap between DEGs and genes near ALS-associated SNP loci, and second by identifying putative DNA regulatory elements disrupted or engendered by ALS-associated SNP variants. The author also compared these DEGs with DEGs from dissected motor neurons from both the SOD1-G93A mice and the iPSC-MNs. In the last model, most DEGs were not replicated. The outcome from this study could be beneficial in exploring the disease mechanisms and genetics underlying ALS. Wang et al performed a systematic review and meta-analysis for diagnostic utility of long non-coding RNAs (lncRNAs) in multiple sclerosis (MS). The authors used the AUADAS-2 tool to test the selected studies from various Chinese and English literature databases. The meta-analysis included data from 12 articles and 24 studies. This study outlined the key role of lncRNAs in the diagnosis of MS, especially in distinguishing relapsing-remitting multiple sclerosis (RRMS) from healthy controls and across various MS subtypes. Thus, the authors demonstrate a critical role of lncRNAs not only in the diagnosis of MS, but also in predicting its progression and assessing disease severity suggesting that the integration of lncRNAs into clinical practice could significantly improve the management and stratification of MS patients, providing a basis for personalized therapeutic strategies.Gao et al reported a two sample Mendelian randomization study on the relationship of interferon-γ and interleukin-18 upstream of intervertebral disc degeneration (IVDD). They used publicly available inflammatory cytokines and IVDD GWAS data and demonstrated the casual relationship between IFN-γ and IL-18 and IVDD via instrumental variable analysis. This study could offer invaluable insights on IVDD monitoring, prevention and screening of drug targets.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,057

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,014
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,002
Communication savante0,0040,005
Science ouverte0,0040,001
Intégrité de la recherche0,0120,014
Charge utile insuffisante (le modèle a refusé de juger)0,0170,016

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,343
Écart entre enseignants0,314 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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