29 research outputs found
A genetic variant of the Wnt receptor LRP6 accelerates synapse degeneration during aging and in Alzheimer's disease
Synapse loss strongly correlates with cognitive decline in Alzheimer's disease (AD), but the underlying mechanisms are poorly understood. Deficient Wnt signaling contributes to synapse dysfunction and loss in AD. Consistently, a variant of the LRP6 receptor, (LRP6-Val), with reduced Wnt signaling, is linked to late-onset AD. However, the impact of LRP6-Val on the healthy and AD brain has not been examined. Knock-in mice, generated by gene editing, carrying this Lrp6 variant develop normally. However, neurons from Lrp6-val mice do not respond to Wnt7a, a ligand that promotes synaptic assembly through the Frizzled-5 receptor. Wnt7a stimulates the formation of the low-density lipoprotein receptor-related protein 6 (LRP6)-Frizzled-5 complex but not if LRP6-Val is present. Lrp6-val mice exhibit structural and functional synaptic defects that become pronounced with age. Lrp6-val mice present exacerbated synapse loss around plaques when crossed to the NL-G-F AD model. Our findings uncover a previously unidentified role for Lrp6-val in synapse vulnerability during aging and AD
Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style
Neuropsychological data about the forms of acquired reading impairment provide a strong basis for the theoretical framework of the dual-route cascade (DRC) model which is predictive of reading performance. However, lesions are often extensive and heterogeneous, thus making it difficult to establish precise functional anatomical correlates. Here, we provide a connective neural account in the aim of accommodating the main principles of the DRC framework and to make predictions on reading skill. We located prominent reading areas using fMRI and applied structural equation modeling to pinpoint distinct neural pathways. Functionality of regions together with neural network dissociations between words and pseudowords corroborate the existing neuroanatomical view on the DRC and provide a novel outlook on the sub-regions involved. In a similar vein, congruent (or incongruent) reliance of pathways, that is reliance on the word (or pseudoword) pathway during word reading and on the pseudoword (or word) pathway during pseudoword reading predicted good (or poor) reading performance as assessed by out-of-magnet reading tests. Finally, inter-individual analysis unraveled an efficient reading style mirroring pathway reliance as a function of the fingerprint of the stimulus to be read, suggesting an optimal pattern of cerebral information trafficking which leads to high reading performance
PREPARATION AND CHARACTERIZATION OF Ge-Si ALLOYS CARRIED OUT BY MOCVD
L'élaboration d'alliages Si-Ge par dépôt chimique en phase vapeur a été entreprise à partir de deux composés organométalliques de formule H3Si-(CH2)2-GeH3 et H3Si-(CH2)3-GeH3. Le comportement thermique de ces composés est étudié par des analyses de la phase gazeuse de décomposition et du matériau solide. Les couches obtenues sont composées de silicium, de germanium e t ne contiennent pas de carbone. En fonction de ces résultats et de caractérisations physico-chimiques des précurseurs et du matériau une discussion nous amène à proposer une structure du matériau.The preparation of Ge-Si alloys by chemical vapour deposition has been undertaken using two organometallic compounds, H3Si-(CH2)2-GeH3 and H3Si -(CH2)3-GeH3. Their thermal behaviour was investigated by analysis of the gaseous products of decomposition and of the solid material. The thin coatings are composed of silicon , germanium and do not contain carbon. According to these results and physico-chemical characterizations of the precursors and the products, we are lead to propose a structure for the solid material
Les marqueurs linguistiques dans l’amélioration du modèle prédictif de la transition vers la schizophrénie
Striatal Synapse Degeneration and Dysfunction Are Reversed by Reactivation of Wnt Signaling
Synapse degeneration in the striatum has been associated with the early stages of Parkinson’s and Huntington’s diseases (PD and HD). However, the molecular mechanisms that trigger synaptic dysfunction and loss are not fully understood. Increasing evidence suggests that deficiency in Wnt signaling triggers synapse degeneration in the adult brain and that this pathway is affected in neurodegenerative diseases. Here, we demonstrate that endogenous Wnt signaling is essential for the integrity of a subset of inhibitory synapses on striatal medium spiny neurons (MSNs). We found that inducible expression of the specific Wnt antagonist Dickkopf-1 (Dkk1) in the adult striatum leads to the loss of inhibitory synapses on MSNs and affects the synaptic transmission of D2-MSNs. We also discovered that re-activation of the Wnt pathway by turning off Dkk1 expression after substantial loss of synapses resulted in the complete recovery of GABAergic and dopamine synapse number. Our results also show that re-activation of the Wnt pathway leads to a recovery of amphetamine response and motor function. Our studies identify the Wnt signaling pathway as a potential therapeutic target for restoring neuronal circuits after synapse degeneration.</jats:p
Euthanasia for Mental Suffering Reduces Stigmatization But May Lead to an Extension of This Practice Without Safeguards
A genetic variant of the Wnt receptor LRP6 accelerates synapse degeneration during ageing and in Alzheimer’s disease
AbstractSynapse loss strongly correlates with cognitive decline in Alzheimer’s Disease (AD), but the underlying mechanisms are poorly understood. Studies suggest that deficient Wnt signalling, a pathway required for neuronal connectivity, contributes to synapse dysfunction and loss in AD. Consistent with this idea, a variant ofLrp6, (Lrp6-val), which confers reduced Wnt signalling, has been linked to late onset AD. However, the impact ofLrp6-valon synapses in the healthy and AD brain has not been examined. Using CRISPR/Cas9 genome editing, we generated a novel knock-in mouse model carrying thisLrp6variant to study its role in synaptic integrity.Lrp6-valmice develop normally and do not exhibit morphological brain abnormalities. Hippocampal neurons fromLrp6-valmice do not respond to Wnt7a, a Wnt ligand that promotes synaptic assembly through the Frizzled-5 (Fz5) receptor. Activation of the Wnt pathway by Wnt ligands leads to the formation of a complex between LRP6 and Fz5. In contrast, LRP6-Val impairs the formation of the LRP6-Fz5 complex elicited by Wnt7a, as detected by proximity ligation assay (PLA). We demonstrate thatLrp6-valmice exhibit structural and functional synaptic defects that become more pronounced with age, consistent with decreased canonical Wnt signalling during ageing. To investigate the contribution of this variant to AD,Lrp6-valmice were crossed tohAPPNL-G-F/NL-G-F(NL-G-F), a knock-in AD mouse model. The presence of theLrp6-valvariant significantly exacerbates synapse loss around amyloid-β plaques inNL-G-Fmice. Our findings uncover a novel role for theLrp6-valvariant in synapse vulnerability during ageing and its contribution to synapse degeneration in AD.</jats:p
A novel algorithm for measuring graph similarity: application to brain networks
International audiencemeasuring similarity among graphs is a challenging issue in many disciplines including neuroscience. Several algorithms, mainly based on vertices or edges properties, were proposed to address this issue. Most of them ignore the physical location of the vertices, which is a crucial factor in the analysis of brain networks. Indeed, functional brain networks are usually represented as graphs composed of vertices (brain regions) connected by edges (functional connectivity). In this paper, we propose a novel algorithm to measure a similarity between graphs. The novelty of our approach is to account for vertices, edges and spatiality at the same time. The proposed algorithm is evaluated using synthetic graphs. It shows high ability to detect and measure similarity between graphs. An application to real functional brain networks is then described. The algorithm allows for quantification of the inter-subjects variability during a picture naming task
