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Dinggang Shen - One of the best experts on this subject based on the ideXlab platform.
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graph kernel based multi task structured feature selection on multi level functional connectivity networks for Brain Disease classification
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Zhengdong Wang, Biao Jie, Mi Wang, Chunxiang Feng, Wen Zhou, Dinggang Shen, Mingxia LiuAbstract:Function connectivity networks (FCNs) based on resting-state functional magnetic resonance imaging (rs-fMRI) have been used for analysis of Brain Diseases, such as Alzheimer’s Disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (e.g., local clustering coefficients) from FCNs as a feature vector for Brain Disease classification, and perform vector-based feature selection methods (e.g., t-test) to improve the performance of learning model, thus ignoring important structural information of FCNs. To address this problem, we propose a graph-kernel-based structured feature selection (gk-MTSFS) method for Brain Disease classification using rs-fMRI data. Different with existing method that focus on vector-based feature selection, our proposed gk-MTSFS method adopts the graph kernel (i.e., kernel constructed on graphs) to preserve the structural information of FCNs, and uses the multi-task learning to explore the complementary information of multi-level thresholded FCNs (i.e., thresholded FCNs with different thresholds). Specifically, in the proposed gk-MTSFS model, we first develop a novel graph-kernel based Laplacian regularizer to preserve the structural information of FCNs. Then, we employ an \(L_{2,1}\)-norm based group sparsity regularizer to joint select a small amount of discriminative features from multi-level FCNs for Brain Disease classification. Experimental results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate the effectiveness of our proposed gk-MTSFS method in rs-fMRI-based Brain Disease diagnosis.
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integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of Brain Disease
Medical Image Analysis, 2018Co-Authors: Biao Jie, Dinggang Shen, Mingxia LiuAbstract:Abstract Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of Brain Disease, such as Alzheimer’s Disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between Brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific Brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific Brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic Brain Disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific Brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of Brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi-task feature learning and multi-kernel learning techniques. Results on 149 subjects with baseline rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that our method can not only improve the classification performance in comparison with state-of-the-art methods, but also provide insights into the spatio-temporal interaction patterns of Brain activity and their changes in Brain disorders.
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landmark based deep multi instance learning for Brain Disease diagnosis
Medical Image Analysis, 2018Co-Authors: Mingxia Liu, Dinggang Shen, Jun Zhang, Ehsan AdeliAbstract:Abstract In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture Brain structural information for Disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective Disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for Brain Disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover Disease-related anatomical landmarks in the Brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.
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deep ensemble learning of sparse regression models for Brain Disease diagnosis
Medical Image Analysis, 2017Co-Authors: Heungil Suk, Dinggang Shen, Seongwhan LeeAbstract:Recent studies on Brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for Brain Disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's Disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.
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deep learning based imaging data completion for improved Brain Disease diagnosis
Medical Image Computing and Computer-Assisted Intervention, 2014Co-Authors: Rongjian Li, Dinggang Shen, Wenlu Zhang, Li Wang, Jiang Li, Shuiwang JiAbstract:Combining multi-modality Brain data for Disease diagnosis commonly leads to improved performance. A challenge in using multi-modality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.
Hiroshi Manya - One of the best experts on this subject based on the ideXlab platform.
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T: Worldwide distribution and broader clinical spectrum of muscle-eye-Brain Disease. Hum Mol Genet 2003
2014Co-Authors: Kiyomi Taniguchi, Hiroshi Manya, Kazuhiro Kobayashi, Kayoko Saito, Hideo Yamanouchi, Akira Ohnuma, Yukiko K Hayashi, Dong Kyu Jin, Munhyang Lee, Enrico ParanoAbstract:Muscle–eye–Brain Disease (MEB), an autosomal recessive disorder prevalent in Finland, is characterized by congenital muscular dystrophy, Brain malformation and ocular abnormalities. Since the MEB phenotype overlaps substantially with those of Fukuyama-type congenital muscular dystrophy (FCMD) and Walker–Warburg syndrome (WWS), these three Diseases are thought to result from a similar pathomechanism. Recently, we showed that MEB is caused by mutations in the protein O-linked mannose b1,2-N-acetylglucosaminyltransferase 1 (POMGnT1) gene. We describe here the identification of seven novel Disease-causing mutations in six of not only non-Finnish Caucasian but also Japanes
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Novel POMGnT1 mutations cause muscle-eye-Brain Disease in Chinese patients.
Molecular genetics and genomics : MGG, 2013Co-Authors: Hui Jiao, Hiroshi Manya, Kazuhiro Kobayashi, Tatsushi Toda, Shuo Wang, Yanzhi Zhang, Jiangxi Xiao, Yanling Yang, Tamao EndoAbstract:Muscle-eye-Brain (MEB) Disease is a congenital muscular dystrophy (CMD) phenotype characterized by hypotonia at birth, Brain structural abnormalities and ocular malformations. To date, few MEB cases have been reported in China where clinical recognition and genetic confirmatory testing on a research basis are recent developments. Here, we report the clinical and molecular genetics of three MEB Disease patients. The patients had different degrees of muscle, eye and Brain symptoms, ranging from congenital hypotonia, early-onset severe myopia and mental retardation to mild weakness, independent walking and language problems. This confirmed the expanding phenotypic spectrum of MEB Disease with varying degrees of hypotonia, myopia and cognitive impairment. Brain magnetic resonance imaging showed cerebellar cysts, hypoplasia and characteristic Brainstem flattening and kinking. Four candidate genes (POMGnT1, FKRP, FKTN and POMT2) were screened, and six POMGnT1 mutations (four novel) were identified, including five missense and one splice site mutation. Pathogenicity of the two novel variants in one patient was confirmed by POMGnT1 enzyme activity assay, protein expression and subcellular localization of mutant POMGnT1 in HeLa cells. Transfected cells harboring this patient's L440R mutant POMGnT1 showed POMGnT1 mislocalization to both the Golgi apparatus and endoplasmic reticulum. We have provided clinical, histological, enzymatic and genetic evidence of POMGnT1 involvement in three unrelated MEB Disease patients in China. The identification of novel POMGnT1 mutations and an expanded phenotypic spectrum contributes to an improved understanding of POMGnT1 structure-function relationships, CMD pathophysiology and genotype-phenotype correlations, while underscoring the need to consider POMGnT1 in Chinese MEB Disease patients.
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loss of function of an n acetylglucosaminyltransferase pomgnt1 in muscle eye Brain Disease
Biochemical and Biophysical Research Communications, 2003Co-Authors: Hiroshi Manya, Keiwa Sakai, Kiyomi Taniguchi, Kazuhiro Kobayashi, Tatsushi Toda, Masao Kawakita, Tamao EndoAbstract:Abstract Muscle–eye–Brain Disease (MEB), an autosomal recessive disorder, is characterized by congenital muscular dystrophy, Brain malformation, and ocular abnormalities. Previously, we found that MEB is caused by mutations in the gene encoding the protein O-linked mannose β1,2-N-acetylglucosaminyltransferase 1 (POMGnT1), which is responsible for the formation of the GlcNAcβ1-2Man linkage of O-mannosyl glycan. Although 13 mutations have been identified in patients with MEB, only the protein with the most frequently observed splicing site mutation has been studied. This protein was found to have no activity. Here, we expressed the remaining mutant POMGnT1s and found that none of them had any activity. These results clearly demonstrate that MEB is inherited as a loss-of-function of POMGnT1.
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worldwide distribution and broader clinical spectrum of muscle eye Brain Disease
Human Molecular Genetics, 2003Co-Authors: Kiyomi Taniguchi, Hiroshi Manya, Kazuhiro Kobayashi, Kayoko Saito, Hideo Yamanouchi, Akira Ohnuma, Yukiko K Hayashi, Dong Kyu Jin, Munhyang Lee, Enrico ParanoAbstract:Muscle-eye-Brain Disease (MEB), an autosomal recessive disorder prevalent in Finland, is characterized by congenital muscular dystrophy, Brain malformation and ocular abnormalities. Since the MEB phenotype overlaps substantially with those of Fukuyama-type congenital muscular dystrophy (FCMD) and Walker-Warburg syndrome (WWS), these three Diseases are thought to result from a similar pathomechanism. Recently, we showed that MEB is caused by mutations in the protein O-linked mannose beta1,2-N-acetylglucosaminyltransferase 1 (POMGnT1) gene. We describe here the identification of seven novel Disease-causing mutations in six of not only non-Finnish Caucasian but also Japanese and Korean patients with suspected MEB, severe FCMD or WWS. Including six previously reported mutations, the 13 Disease-causing mutations we have found thus far are dispersed throughout the entire POMGnT1 gene. We also observed a slight correlation between the location of the mutation and clinical severity in the Brain: patients with mutations near the 5' terminus of the POMGnT1 coding region show relatively severe Brain symptoms such as hydrocephalus, while patients with mutations near the 3' terminus have milder phenotypes. Our results indicate that MEB may exist in population groups outside of Finland, with a worldwide distribution beyond our expectations, and that the clinical spectrum of MEB is broader than recognized previously. These findings emphasize the importance of considering MEB and searching for POMGnT1 mutations in WWS or other congenital muscular dystrophy patients worldwide.
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Deficiency of alpha-dystroglycan in muscle-eye-Brain Disease.
Biochemical and biophysical research communications, 2002Co-Authors: Hiroki Kano, Hiroshi Manya, Kazuhiro Kobayashi, Masaji Tachikawa, Ichizo Nishino, Volker Straub, Beril Talim, Ralf Herrmann, Ikuya Nonaka, Thomas VoitAbstract:Alpha-dystroglycan is a component of the dystrophin-glycoprotein-complex, which is the major mechanism of attachment between the cytoskeleton and the extracellular matrix. Muscle-eye-Brain Disease (MEB) is an autosomal recessive disorder characterized by congenital muscular dystrophy, ocular abnormalities and lissencephaly. We recently found that MEB is caused by mutations in the protein O-linked mannose beta1,2-N-acetylglucosaminyltransferase (POMGnT1) gene. POMGnT1 is a glycosylation enzyme that participates in the synthesis of O-mannosyl glycan, a modification that is rare in mammals but is known to be a laminin-binding ligand of alpha-dystroglycan. Here we report a selective deficiency of alpha-dystroglycan in MEB patients. This finding suggests that alpha-dystroglycan is a potential target of POMGnT1 and that altered glycosylation of alpha-dystroglycan may play a critical role in the pathomechanism of MEB and some forms of muscular dystrophy.
Meike W Vernooij - One of the best experts on this subject based on the ideXlab platform.
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thyroid function and the risk of dementia the rotterdam study
Neurology, 2016Co-Authors: Layal Chaker, Tim I M Korevaar, Peter J. Koudstaal, Albert Hofman, Abbas Dehghan, Frank J Wolters, Daniel Bos, Aad Van Der Lugt, Oscar H Franco, Meike W VernooijAbstract:Objective: To study the role of thyroid function in dementia, cognitive function, and subclinical vascular Brain Disease with MRI. Methods: Analyses were performed within the Rotterdam Study (baseline 1997), a prospective, population-based cohort. We evaluated the association of thyroid-stimulating hormone (TSH) and free thyroxine with incident dementia using Cox models adjusted for age, sex, cardiovascular risk factors, and education. Absolute risks were calculated accounting for death as a competing risk factor. Associations of thyroid function with cognitive test scores and subclinical vascular Brain Disease (white matter lesions, lacunes, and microbleeds) were assessed with linear or logistic regression. Additionally, we stratified by sex and restricted analyses to normal thyroid function. Results: We included 9,446 participants with a mean age of 65 years. During follow-up (mean 8.0 years), 601 participants had developed dementia. Higher TSH was associated with lower dementia risk in both the full and normal ranges of thyroid function (hazard ratio [HR] 0.90, 95% confidence interval [CI] 0.83–0.98; and HR 0.76, 95% CI 0.64–0.91, respectively). This association was independent of cardiovascular risk factors. Dementia risk was higher in individuals with higher free thyroxine (HR 1.04, 95% CI 1.01–1.07). Absolute 10-year dementia risk decreased from 15% to 10% with higher TSH in older women. Higher TSH was associated with better global cognitive scores ( p = 0.021). Thyroid function was not related to subclinical vascular Brain Disease as indicated by MRI. Conclusions: High and high-normal thyroid function is associated with increased dementia risk. Thyroid function is not related to vascular Brain Disease as assessed by MRI, suggesting a role for thyroid hormone in nonvascular pathways leading to dementia.
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kidney function and cerebral small vessel Disease in the general population
International Journal of Stroke, 2015Co-Authors: Saloua Akoudad, Sanaz Sedaghat, Aad Van Der Lugt, Peter J. Koudstaal, Arfan M Ikram, Albert Hofman, Meike W VernooijAbstract:BackgroundAnatomic and hemodynamic similarities between renal and cerebral vessels suggest a tight link between kidney Disease and Brain Disease. Although several distinct markers are used to identify subclinical kidney and Brain Disease, a comprehensive assessment of how these markers link damage at both end organs is lacking.AimTo investigate whether measures of kidney function were associated with cerebral small vessel Disease on MRI.MethodsIn 2526 participants of the population-based Rotterdam Study, we measured urinary albumin-to-creatinine ratio, and estimated glomerular filtration rate based on serum creatinine and cystatin C. All participants underwent Brain magnetic resonance imaging. We assessed presence of cerebral small vessel Disease by calculating white matter lesion volumes and rating the presence of lacunes and cerebral microbleeds. We used multivariable linear and logistic regression to investigate the association between kidney function and cerebral small vessel Disease.ResultsWorse kidn...
Kazuhiro Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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T: Worldwide distribution and broader clinical spectrum of muscle-eye-Brain Disease. Hum Mol Genet 2003
2014Co-Authors: Kiyomi Taniguchi, Hiroshi Manya, Kazuhiro Kobayashi, Kayoko Saito, Hideo Yamanouchi, Akira Ohnuma, Yukiko K Hayashi, Dong Kyu Jin, Munhyang Lee, Enrico ParanoAbstract:Muscle–eye–Brain Disease (MEB), an autosomal recessive disorder prevalent in Finland, is characterized by congenital muscular dystrophy, Brain malformation and ocular abnormalities. Since the MEB phenotype overlaps substantially with those of Fukuyama-type congenital muscular dystrophy (FCMD) and Walker–Warburg syndrome (WWS), these three Diseases are thought to result from a similar pathomechanism. Recently, we showed that MEB is caused by mutations in the protein O-linked mannose b1,2-N-acetylglucosaminyltransferase 1 (POMGnT1) gene. We describe here the identification of seven novel Disease-causing mutations in six of not only non-Finnish Caucasian but also Japanes
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Novel POMGnT1 mutations cause muscle-eye-Brain Disease in Chinese patients.
Molecular genetics and genomics : MGG, 2013Co-Authors: Hui Jiao, Hiroshi Manya, Kazuhiro Kobayashi, Tatsushi Toda, Shuo Wang, Yanzhi Zhang, Jiangxi Xiao, Yanling Yang, Tamao EndoAbstract:Muscle-eye-Brain (MEB) Disease is a congenital muscular dystrophy (CMD) phenotype characterized by hypotonia at birth, Brain structural abnormalities and ocular malformations. To date, few MEB cases have been reported in China where clinical recognition and genetic confirmatory testing on a research basis are recent developments. Here, we report the clinical and molecular genetics of three MEB Disease patients. The patients had different degrees of muscle, eye and Brain symptoms, ranging from congenital hypotonia, early-onset severe myopia and mental retardation to mild weakness, independent walking and language problems. This confirmed the expanding phenotypic spectrum of MEB Disease with varying degrees of hypotonia, myopia and cognitive impairment. Brain magnetic resonance imaging showed cerebellar cysts, hypoplasia and characteristic Brainstem flattening and kinking. Four candidate genes (POMGnT1, FKRP, FKTN and POMT2) were screened, and six POMGnT1 mutations (four novel) were identified, including five missense and one splice site mutation. Pathogenicity of the two novel variants in one patient was confirmed by POMGnT1 enzyme activity assay, protein expression and subcellular localization of mutant POMGnT1 in HeLa cells. Transfected cells harboring this patient's L440R mutant POMGnT1 showed POMGnT1 mislocalization to both the Golgi apparatus and endoplasmic reticulum. We have provided clinical, histological, enzymatic and genetic evidence of POMGnT1 involvement in three unrelated MEB Disease patients in China. The identification of novel POMGnT1 mutations and an expanded phenotypic spectrum contributes to an improved understanding of POMGnT1 structure-function relationships, CMD pathophysiology and genotype-phenotype correlations, while underscoring the need to consider POMGnT1 in Chinese MEB Disease patients.
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loss of function of an n acetylglucosaminyltransferase pomgnt1 in muscle eye Brain Disease
Biochemical and Biophysical Research Communications, 2003Co-Authors: Hiroshi Manya, Keiwa Sakai, Kiyomi Taniguchi, Kazuhiro Kobayashi, Tatsushi Toda, Masao Kawakita, Tamao EndoAbstract:Abstract Muscle–eye–Brain Disease (MEB), an autosomal recessive disorder, is characterized by congenital muscular dystrophy, Brain malformation, and ocular abnormalities. Previously, we found that MEB is caused by mutations in the gene encoding the protein O-linked mannose β1,2-N-acetylglucosaminyltransferase 1 (POMGnT1), which is responsible for the formation of the GlcNAcβ1-2Man linkage of O-mannosyl glycan. Although 13 mutations have been identified in patients with MEB, only the protein with the most frequently observed splicing site mutation has been studied. This protein was found to have no activity. Here, we expressed the remaining mutant POMGnT1s and found that none of them had any activity. These results clearly demonstrate that MEB is inherited as a loss-of-function of POMGnT1.
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worldwide distribution and broader clinical spectrum of muscle eye Brain Disease
Human Molecular Genetics, 2003Co-Authors: Kiyomi Taniguchi, Hiroshi Manya, Kazuhiro Kobayashi, Kayoko Saito, Hideo Yamanouchi, Akira Ohnuma, Yukiko K Hayashi, Dong Kyu Jin, Munhyang Lee, Enrico ParanoAbstract:Muscle-eye-Brain Disease (MEB), an autosomal recessive disorder prevalent in Finland, is characterized by congenital muscular dystrophy, Brain malformation and ocular abnormalities. Since the MEB phenotype overlaps substantially with those of Fukuyama-type congenital muscular dystrophy (FCMD) and Walker-Warburg syndrome (WWS), these three Diseases are thought to result from a similar pathomechanism. Recently, we showed that MEB is caused by mutations in the protein O-linked mannose beta1,2-N-acetylglucosaminyltransferase 1 (POMGnT1) gene. We describe here the identification of seven novel Disease-causing mutations in six of not only non-Finnish Caucasian but also Japanese and Korean patients with suspected MEB, severe FCMD or WWS. Including six previously reported mutations, the 13 Disease-causing mutations we have found thus far are dispersed throughout the entire POMGnT1 gene. We also observed a slight correlation between the location of the mutation and clinical severity in the Brain: patients with mutations near the 5' terminus of the POMGnT1 coding region show relatively severe Brain symptoms such as hydrocephalus, while patients with mutations near the 3' terminus have milder phenotypes. Our results indicate that MEB may exist in population groups outside of Finland, with a worldwide distribution beyond our expectations, and that the clinical spectrum of MEB is broader than recognized previously. These findings emphasize the importance of considering MEB and searching for POMGnT1 mutations in WWS or other congenital muscular dystrophy patients worldwide.
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Deficiency of alpha-dystroglycan in muscle-eye-Brain Disease.
Biochemical and biophysical research communications, 2002Co-Authors: Hiroki Kano, Hiroshi Manya, Kazuhiro Kobayashi, Masaji Tachikawa, Ichizo Nishino, Volker Straub, Beril Talim, Ralf Herrmann, Ikuya Nonaka, Thomas VoitAbstract:Alpha-dystroglycan is a component of the dystrophin-glycoprotein-complex, which is the major mechanism of attachment between the cytoskeleton and the extracellular matrix. Muscle-eye-Brain Disease (MEB) is an autosomal recessive disorder characterized by congenital muscular dystrophy, ocular abnormalities and lissencephaly. We recently found that MEB is caused by mutations in the protein O-linked mannose beta1,2-N-acetylglucosaminyltransferase (POMGnT1) gene. POMGnT1 is a glycosylation enzyme that participates in the synthesis of O-mannosyl glycan, a modification that is rare in mammals but is known to be a laminin-binding ligand of alpha-dystroglycan. Here we report a selective deficiency of alpha-dystroglycan in MEB patients. This finding suggests that alpha-dystroglycan is a potential target of POMGnT1 and that altered glycosylation of alpha-dystroglycan may play a critical role in the pathomechanism of MEB and some forms of muscular dystrophy.
C. Diesen - One of the best experts on this subject based on the ideXlab platform.
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pomgnt1 mutation and phenotypic spectrum in muscle eye Brain Disease
Journal of Medical Genetics, 2004Co-Authors: C. Diesen, Bru Cormand, Helena Pihko, Anneriitta Saarinen, C Rosenlew, William B Dobyns, J Dieguez, Leena Valanne, Tarja Joensuu, Annaelina LehesjokiAbstract:Muscle-eye-Brain Disease (MEB; OMIM 253280) was first described in 1977 in Finland,1 where it is enriched because of founder effect and genetic isolation.2 MEB is now known to occur throughout the world, but Finland remains the country with the largest group of MEB patients. MEB patients present as floppy infants with visual problems and severe mental retardation. The hypotonia is partly caused by muscular dystrophy and partly by cerebral dysfunction. Hypotonia is replaced by spasticity and contractures with increasing age.1,3 Visual failure is the result of progressive myopia, retinal degeneration, and congenital glaucoma. Juvenile cataracts develop by the age of 10 years. The presence of giant visual evoked potentials is an important diagnostic feature.4 The typical central nervous system malformation revealed by magnetic resonance imaging (MRI), referred to as “cobblestone complex”,5 consists of cobblestone cortex, midline deformities, flat Brain stem, mild cerebellar hypoplasia, and cerebellar cortical cysts.6 Microscopically the cortex is disorganised, with an overgrowth of glia forming a thick membrane on the Brain surface.7 The combination of muscular dystrophy and a severe neuronal migration defect is not exclusive for MEB, but is also seen in Walker–Warburg syndrome (WWS; OMIM 2366708) and Fukuyama congenital muscular dystrophy (FCMD; OMIM 2538009). The recent molecular genetic findings have provided an explanation as to why the distinct clinical features are partially shared in these three Diseases. The MEB gene encodes a protein O -mannose b-1, 2- N -acetylglucosaminyltransferase (POMGnT1).10 Mutations in another enzyme involved in O -mannosylation, the O -mannosyltransferase (POMT1), were recently found in a group of WWS patients.11 Fukutin, encoded by the FCMD gene,12 is strongly suspected to play a role in glycosylation.13 The unifying feature in all these disorders is deficient post-translational glycosylation of α-dystroglycan,11,14– …
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mutations in the fkrp gene can cause muscle eye Brain Disease and walker warburg syndrome
Journal of Medical Genetics, 2004Co-Authors: Beltran Valero D De Bernabe, Thomas Voit, Alice Steinbrecher, Cheryl Longman, Y Yuva, Volker Straub, R. Herrmann, J. Sperner, C.g. Korenke, C. DiesenAbstract:The hypoglycosylation of α-dystroglycan is a new Disease mechanism recently identified in four congenital muscular dystrophies (CMDs): Walker–Warburg syndrome (WWS), muscle-eye-Brain Disease (MEB), Fukuyama CMD (FCMD), and CMD type 1C (MDC1C).1 The underlying genetic defects in these disorders are mutations in known or putative glycosyltransferase enzymes, which among their targets probably include α-dystroglycan. FCMD (MIM: 253800) is caused by mutations in fukutin2; MEB (MEB [MIM 236670]) is due to mutations in POMGnT13; and in WWS (WWS [MIM: 236670]) POMT1 is mutated.4 In addition to the Brain abnormalities, both MEB and WWS have structural eye involvement. In FCMD, eye involvement is more variable, ranging from myopia to retinal detachment, persistent primary vitreous body, persistent hyaloid artery, or microphthalmos.5 WWS, MEB, and FCMD display type II or cobblestone lissencephaly, in which the main abnormality is different degrees of Brain malformation secondary at least in part to the overmigration of heterotopic neurones into the leptominenges through gaps in the external (pial) basement membrane.6,7 Whereas there are broad similarities between WWS and MEB, clear diagnostic criteria differentiating between these two conditions have been proposed8 and are shown as clinical features in table 1. A similar combination of muscular dystrophy and cobblestone lissencephaly is also found in the myodystrophy mouse (myd, renamed Largemyd), in which the Large gene is mutated.6,9,10 Our group has very recently identified mutations in the human LARGE gene in a patient with a novel form of CMD (MDC1D).11 View this table: Table 1 Clinical features of patients 1 and 2, compared with MEB and WWS patients with confirmed mutations in POGnT1 and POMT1, respectively The gene encoding the fukutin related protein (FKRP, [MIM 606612]) is mutated in a severe form of CMD (MDC1C, [OMIM 606612]).12 Clinical features of MDC1C are …