Genotype-Phenotype Correlation of the Childhood-Onset Bethlem Myopathy in the Mediterranean Region of Turkey

Purpose of review In the last 30 years, there have many publications describing the pattern of muscle involvement of different neuromuscular diseases leading to an increase in the information available for diagnosis. A high degree of expertise is needed to remember all the patterns described. Some attempts to use artificial intelligence or analysing muscle MRIs have been developed. We review the main patterns of involvement in limb girdle muscular dystrophies (LGMDs) and summarize the strategies for using artificial intelligence tools in this field. Recent findings The most frequent LGMDs have a widely described pattern of muscle involvement; however, for those rarer diseases, there is still not too much information available. patients. Most of the articles still include only pelvic and lower limbs muscles, which provide an incomplete picture of the diseases. AI tools have efficiently demonstrated to predict diagnosis of a limited number of disease with high accuracy. Summary Muscle MRI continues being a useful tool supporting the diagnosis of patients with LGMD and other neuromuscular diseases. However, the huge variety of patterns described makes their use in clinics a complicated task. Artificial intelligence tools are helping in that regard and there are already some accessible machine learning algorithms that can be used by the global medical community.

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Segregation of the COL6A2 Variant (c.1817-3C>G) in a Consanguineous Saudi Family with Bethlem Myopathy

Aldharee,

Hamdan

2024

Genes

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Introduction: Bethlem myopathy is a rare genetic disease caused by a variant mapped to 21q22, which harbors the collagen type VI alpha 2 chain (COL6A2) and collagen type VI alpha 1 chain (COL6A1) genes, and 2q37, which harbors the collagen type VI alpha 3 chain (COL6A3) gene. Disease onset can occur at any age, and the symptoms are related to those of muscular dystrophy. Since Bethlem myopathy is a rare disease, no previous studies have been conducted in Arab countries, including Saudi Arabia. Its variable presentation of nonspecific muscular contractions and severity represents a diagnostic dilemma. Case presentation: Here, we report a Saudi pediatric patient, who is 9 years old (proband), brought to the pediatric clinic of King Saud’s Hospital by his mother. The boy presented with difficulty standing, walking, and running with his classmates and unaffected siblings. He has a younger sibling, aged 6 years old, who reported having a limping gait and difficulty bending his right knee. Laboratory results for the proband were unremarkable except for a slight increase in creatine kinase (CK). Whole-exome sequencing (WES) was performed for five family members, including the proband and his symptomatic brother, their mother and two asymptomatic siblings. A very rare 3′ splice site acceptor intronic variant, NM_001849.4: c.1817-3C>G, located three nucleotides before exon 25, was identified in COL6A2. Bioinformatics tools (SpliceAI, dbscSNV, FATHMM-MKL, and MaxEntScan) predicted this variant as pathogenic. The proband and his 6-year-old sibling presented a homozygous genotype for the variant, whereas the mother and one asymptomatic sibling were heterozygous, and the other sibling carried homozygous wild-type alleles. Conclusions: This is the first study to report a case of Bethlem myopathy confirmed by WES in Saudi Arabia and all Arab nations. The identified variant is rare, and its segregation pattern suggests autosomal recessive inheritance. The segregation pattern and bioinformatics tool results may qualify this variant to be annotated as pathogenic, addressing the reported uncertainty of its classification. Our findings contribute to linking and filling the knowledge gap of diagnosing and managing patients with collagen VI-related myopathies, providing greater clinical and genetic understanding to the existing knowledge.

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Genotype-Phenotype Correlation of the Childhood-Onset Bethlem Myopathy in the Mediterranean Region of Turkey

Cited by 3 publications

References 19 publications

Limb-Girdle Muscular Dystrophy, Autosomal Dominant (LGMDD)

Limb-Girdle Muscular Dystrophy, Autosomal Dominant (LGMDD)

MRI for the diagnosis of limb girdle muscular dystrophies

Segregation of the COL6A2 Variant (c.1817-3C>G) in a Consanguineous Saudi Family with Bethlem Myopathy

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