No abstract

The X-chromosomal-linked lysosomal storage disorder Fabry disease can lead to life-threatening manifestations. The pathological significance of the Fabry mutation D313Y is doubted, because, in general, D313Y patients do not present clinical manifestations conformable with Fabry disease. This is in contrast to the analysis of the alphagalactosidase A activity, which is reduced in D313Y patients. We report a comprehensive clinical, biochemical and molecular genetic analysis of two patients with a D313Y mutation. The alpha-galactosidase A activity was reduced in both patients. No Fabry symptoms or Fabry organ involvement was detected in these patients. The new biomarker lyso-Gb3, severely increased in classical Fabry patients, was determined and in both patients lyso-Gb3 was below the average of a normal population.Our data for the first time not only clinically but also biochemically supports the hypothesis that the D313Y mutation is not a classical one, but a rare variant mutation.

The endoplasmic reticulum enzyme fatty acid 2-hydroxylase (FA2H) plays a major role in the formation of 2-hydroxy glycosphingolipids, main components of myelin. FA2H deficiency in mice leads to severe central demyelination and axon loss. In humans it has been associated with phenotypes from the neurodegeneration with brain iron accumulation (fatty acid hydroxylase-associated neurodegeneration, FAHN), hereditary spastic paraplegia (HSP type SPG35) and leukodystrophy (leukodystrophy with spasticity and dystonia) spectrum. We performed an in-depth clinical and retrospective neurophysiological and imaging study in a cohort of 19 cases with biallelic FA2H mutations. FAHN/SPG35 manifests with early childhood onset predominantly lower limb spastic tetraparesis and truncal instability, dysarthria, dysphagia, cerebellar ataxia, and cognitive deficits, often accompanied by exotropia and movement disorders. The disease is rapidly progressive with loss of ambulation after a median of 7 years after disease onset and demonstrates little interindividual variability. The hair of FAHN/SPG35 patients shows a bristle-like appearance; scanning electron microscopy of patient hair shafts reveals deformities (longitudinal grooves) as well as plaque-like adhesions to the hair, likely caused by an abnormal sebum composition also described in a mouse model of FA2H deficiency. Characteristic imaging features of FAHN/SPG35 can be summarized by the 'WHAT' acronym: white matter changes, hypointensity of the globus pallidus, ponto-cerebellar atrophy, and thin corpus callosum. At least three of four imaging features are present in 85% of FA2H mutation carriers. Here, we report the first systematic, large cohort study in FAHN/SPG35 and determine the phenotypic spectrum, define the disease course and identify clinical and imaging biomarkers.

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume ( r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study ( N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.