Back

Characterization of a novel variant in the HR1 domain of MFN2 in a patient with ataxia, optic atrophy and sensorineural hearing loss

Sharma, G.; Sabouny, R.; Joel, M.; Martens, K.; de Koning, J.; Martino, D.; Pfeffer, G.; Shutt, T. E.

2021-01-11 genetics
10.1101/2021.01.11.426268 bioRxiv
Show abstract

Pathogenic variants in MFN2 cause Charcot-Marie-Tooth disease (CMT) type 2A (CMT2A) and are the leading cause of the axonal subtypes of CMT. CMT2A is characterized by predominantly distal motor weakness and muscle atrophy, with highly variable severity and onset age. Notably, some MFN2 variants can also lead to other phenotypes such as optic atrophy, hearing loss and lipodystrophy. Despite the clear link between MFN2 and CMT2A, our mechanistic understanding of how dysfunction of the MFN2 protein causes human disease pathologies remains incomplete. This lack of understanding is due in part to the multiple cellular roles of MFN2. Though initially characterized for its role in mediating mitochondrial fusion, MFN2 also plays important roles in mediating interactions between mitochondria and other organelles, such as the endoplasmic reticulum and lipid droplets. Additionally, MFN2 is also important for mitochondrial transport, mitochondrial autophagy, and has even been implicated in lipid transfer. Though over 100 pathogenic MFN2 variants have been described to date, only a few have been characterized functionally, and even then, often only for one or two functions. Here, we describe a novel homozygous MFN2 variant, D414V, in a patient presenting with cerebellar ataxia, deafness, blindness, and diffuse cerebral and cerebellar atrophy. Characterization of patient fibroblasts reveals phenotypes consistent with impaired MFN2 functions and expands the phenotypic presentation of MFN2 variants to include cerebellar ataxia.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.