Identification of elongated cilia and chiral malformation in TMEM67 mutant brains

poster abstractTransmembrane protein 67 (TMEM67) is encoded by one of four syndromic encephalocele genes. In humans a mutation in TMEM67 causes Meckel Gruber Syndrome, type 3 (MKS3) which is characterized by severe encephalocele and cystic kidneys and is usually fatal in the neonatal period. MKS3 is one of a spectrum of diseases known as ciliopathies because the proteins responsible for the disease are found in cells with the primary cilia. Primary cilia are a single, hair-like organelle that is found on the apical membrane of polarized cells and is thought to be involved in formation of left-right asymmetry during development as well as mechano- and chemo-reception. Here we characterize previously unreported details of cerebral phenotype in the Wistar polycystic kidney (Wpk) rats with a TMEM67 mutation. In choroid plexus (CP) epithelia of wild type animals, TMEM67 localizes to the plasma membrane and to a region close to the basal side of CP primary cilia. In a choroid plexus cell line that forms an epithelial sheet, the TMEM67 is found intracellularly but also localizes to the junctional complexes as evidenced by β catenin co-localization. Absence of normal TMEM67 leads to elongation of primary cilia in the ependymal cells lining the cerebral ventricles of the TMEM67-/- animals indicating that this protein is involved in the regulation of cilia length. Reduced aqueduct, bilateral dilatation with fusion of lateral ventricles, swelling of the hippocampus, and altered hindbrain histoarchitecture are noted in the TMEM67-/- rats. In the heterozygous animals mild asymmetric ventriculomegaly primarily on the left side is observed during early postnatal periods and continues into adulthood. These results suggest that TMEM67 is required for cilia length control and normal development of cerebral midline that maintains the symmetry of the left and right hemispheres. The Wpk rat model, orthologous to human MKS3, provides a unique model in which to study the development of both severe (TMEM67-/-) and mild (TMEM67+/-) hydrocephalus and other developmental abnormalities that are commonly found in human patients with ciliopathies

New Frontiers Titan Orbiter

International audienceAs one of two planetary objects (other than Earth) that have solid surfaces, thick atmospheres, and astrobiological significance, Titan, like Mars, merits ongoing study with multiple spacecraft. We propose that a Titan orbiter dedicated to geophysics, geology, and atmospheric science be added to the New Frontiers menu for the coming decade

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset-9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset-9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease