Biallelic SQSTM1 mutations in early-onset, variably progressive neurodegeneration.

OBJECTIVE: To characterize clinically and molecularly an early-onset, variably progressive neurodegenerative disorder characterized by a cerebellar syndrome with severe ataxia, gaze palsy, dyskinesia, dystonia, and cognitive decline affecting 11 individuals from 3 consanguineous families. METHODS: We used whole-exome sequencing (WES) (families 1 and 2) and a combined approach based on homozygosity mapping and WES (family 3). We performed in vitro studies to explore the effect of the nontruncating SQSTM1 mutation on protein function and the effect of impaired SQSTM1 function on autophagy. We analyzed the consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in vivo using zebrafish as a model. RESULTS: We identified 3 homozygous inactivating variants, including a splice site substitution (c.301+2T>A) causing aberrant transcript processing and accelerated degradation of a resulting protein lacking exon 2, as well as 2 truncating changes (c.875_876insT and c.934_936delinsTGA). We show that loss of SQSTM1 causes impaired production of ubiquitin-positive protein aggregates in response to misfolded protein stress and decelerated autophagic flux. The consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in zebrafish documented a variable but reproducible phenotype characterized by cerebellum anomalies ranging from depletion of axonal connections to complete atrophy. We provide a detailed clinical characterization of the disorder; the natural history is reported for 2 siblings who have been followed up for >20 years. CONCLUSIONS: This study offers an accurate clinical characterization of this recently recognized neurodegenerative disorder caused by biallelic inactivating mutations in SQSTM1 and links this phenotype to defective selective autophagy

Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition

Purpose of Review We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results. Recent Findings The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion

Phenotypic continuum of NFU1-related disorders.

Bi-allelic variants in Iron-Sulfur Cluster Scaffold (NFU1) have previously been associated with multiple mitochondrial dysfunctions syndrome 1 (MMDS1) characterized by early-onset rapidly fatal leukoencephalopathy. We report 19 affected individuals from 10 independent families with ultra-rare bi-allelic NFU1 missense variants associated with a spectrum of early-onset pure to complex hereditary spastic paraplegia (HSP) phenotype with a longer survival (16/19) on one end and neurodevelopmental delay with severe hypotonia (3/19) on the other. Reversible or irreversible neurological decompensation after a febrile illness was common in the cohort, and there were invariable white matter abnormalities on neuroimaging. The study suggests that MMDS1 and HSP could be the two ends of the NFU1-related phenotypic continuum

Network connectivity determines cortical thinning in early Parkinson's disease progression.

Here we test the hypothesis that the neurodegenerative process in Parkinson's disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson's Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a "disease reservoir" evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer's disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment

Predictive model of spread of Parkinson's pathology using network diffusion

Growing evidence suggests that a "prion-like" mechanism underlies the pathogenesis of many neurodegenerative disorders, including Parkinson's disease (PD). We extend and tailor previously developed quantitative and predictive network diffusion model (NDM) to PD, by specifically modeling the trans-neuronal spread of alpha-synuclein outward from the substantia nigra (SN). The model demonstrated the spatial and temporal patterns of PD from neuropathological and neuroimaging studies and was statistically validated using MRI deformation of 232 Parkinson's patients. After repeated seeding simulations, the SN was found to be the most likely seed region, supporting its unique lynchpin role in Parkinson's pathology spread. Other alternative spread models were also evaluated for comparison, specifically, random spread and distance-based spread; the latter tests for Braak's original caudorostral transmission theory. We showed that the distance-based spread model is not as well supported as the connectivity-based model. Intriguingly, the temporal sequencing of affected regions predicted by the model was in close agreement with Braak stages III-VI, providing what we consider a "computational Braak" staging system. Finally, we investigated whether the regional expression patterns of implicated genes contribute to regional atrophy. Despite robust evidence for genetic factors in PD pathogenesis, NDM outperformed regional genetic expression predictors, suggesting that network processes are far stronger mediators of regional vulnerability than innate or cell-autonomous factors. This is the first finding yet of the ramification of prion-like pathology propagation in Parkinson's, as gleaned from in vivo human imaging data. The NDM is potentially a promising robust and clinically useful tool for diagnosis, prognosis and staging of PD

Network connectivity determines cortical thinning in early Parkinson's disease progression

Here we test the hypothesis that the neurodegenerative process in Parkinson's disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson's Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a "disease reservoir" evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer's disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment

Neuroanatomical changes in white and grey matter after sleeve gastrectomy

International audienceBACKGROUND: MRI studies show that obese adults have reduced grey matter (GM) and white matter (WM) tissue density as well as altered WM integrity. Bariatric surgery can lead to substantial weight loss and improvements in metabolic parameters, but it remains to be examined if it induces structural brain changes. The aim of this study was to characterize GM and WM density changes measured with MRI in a longitudinal setting following sleeve gastrectomy, and to determine whether any changes are related to inflammation and cardiometabolic blood markers. METHODS: 29 participants with obesity (age: 45.9 ​± ​7.8 years) scheduled to undergo sleeve gastrectomy were recruited. High-resolution T1-weighted anatomical images were acquired 1 month prior to as well as 4 and 12 months after surgery. GM and WM densities were quantified using voxel-based morphometry (VBM). Circulating lipid profile, glucose, insulin and inflammatory markers (interleukin-6, C-reactive protein and lipopolysaccharide-binding protein) were measured at each time point. A linear mixed effect model was used to compare brain changes before and after SG, controlling for age, sex, initial BMI and diabetic status. To assess the associations between changes in adiposity, metabolism and inflammation and changes in GM or WM density, the mean GM and WM densities were extracted across all the participants using atlas-derived regions of interest, and linear mixed-effect models were used. RESULTS: As expected, weight, BMI, waist circumference and neck circumference significantly decreased after SG compared with baseline (p ​\textless ​0.001 for all). A widespread increase in WM density was observed after surgery, particularly in the cerebellum, brain stem, cerebellar peduncle, cingulum, corpus callosum and corona radiata (p ​\textless ​0.05, after FDR correction). Significant increases in GM density were observed 4 months after SG compared to baseline in several brain regions such as the bilateral occipital cortex, temporal cortex, postcentral gyrus, cerebellum, hippocampus and insula as well as right fusiform gyrus, right parahippocampal gyrus, right lingual gyrus and right amygdala. These GM and WM increases were more pronounced and widespread after 12 months and were significantly associated with post-operative weight loss and the improvement of metabolic alterations. A linear mixed-effect model also showed associations between post-operative reductions in lipopolysaccharide-binding protein, a marker of inflammation, and increased WM density. To confirm our results, we tested whether the peak of each significant region showed BMI-related differences in an independent dataset (Human Connectome Project). We matched a group of individuals who were severely obese with a group of individuals who were lean for age, sex and ethnicity. Severe obesity was associated with reduced WM density in the brain stem and cerebellar peduncle as well as reduced GM density in cerebellum, regions that significantly changed after surgery (p ​\textless ​0.01 for all clusters). CONCLUSIONS: Bariatric surgery-induced weight loss and improvement in metabolic alterations is associated with widespread increases in WM and GM densities. These post-operative changes overlapped with baseline brain differences between participants who were severely obese and those who were normal-weight in a separate dataset, which may suggest a recovery of WM and GM alterations after bariatric surgery

A machine learning-based system for detecting leishmaniasis in microscopic images

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65 recall and 50 precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52 and 71, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods