Final Exon Frameshift Biallelic PTPN23 Variants Are Associated with Microcephalic Complex Hereditary Spastic Paraplegia.
Reham Khalaf-NazzalJames FashamNishanka UbeyratnaDavid J EvansJoseph S LeslieThomas T WarnerFida' Al-HijawiShurouq AlshaerWisam BakerPeter D TurnpennyEmma L BapleAndrew H CrosbyPublished in: Brain sciences (2021)
The hereditary spastic paraplegias (HSPs) are a large clinically heterogeneous group of genetic disorders classified as 'pure' when the cardinal feature of progressive lower limb spasticity and weakness occurs in isolation and 'complex' when associated with other clinical signs. Here, we identify a homozygous frameshift alteration occurring in the last coding exon of the protein tyrosine phosphatase type 23 (PTPN23) gene in an extended Palestinian family associated with autosomal recessive complex HSP. PTPN23 encodes a catalytically inert non-receptor protein tyrosine phosphatase that has been proposed to interact with the endosomal sorting complex required for transport (ESCRT) complex, involved in the sorting of ubiquitinated cargos for fusion with lysosomes. In view of our data, we reviewed previously published candidate pathogenic PTPN23 variants to clarify clinical outcomes associated with pathogenic gene variants. This determined that a number of previously proposed candidate PTPN23 alterations are likely benign and revealed that pathogenic biallelic PTPN23 alterations cause a varied clinical spectrum comprising of complex HSP associated with microcephaly, which may occur without intellectual impairment or involve more severe neurological disease. Together, these findings highlight the importance of the inclusion of the PTPN23 gene on HSP gene testing panels globally.
Keyphrases
- copy number
- genome wide
- intellectual disability
- heat shock
- lower limb
- heat shock protein
- zika virus
- heat stress
- dna methylation
- botulinum toxin
- multiple sclerosis
- big data
- systematic review
- genome wide identification
- oxidative stress
- randomized controlled trial
- early onset
- deep learning
- small molecule
- binding protein
- protein kinase