Cell-Autonomous Alterations in Dendritic Arbor Morphology and Connectivity Induced by Overexpression of MeCP2 in Xenopus Central Neurons In Vivo

Methyl CpG binding protein-2 (MeCP2) is an essential epigenetic regulator in human brain development. Mutations in the MeCP2 gene have been linked to Rett syndrome, a severe X-linked progressive neurodevelopmental disorder, and one of the most common causes of mental retardation in females. MeCP2 duplication and triplication have also been found to affect brain development, indicating that both loss of function and gain in MeCP2 dosage lead to similar neurological phenotypes. Here, we used the Xenopus laevis visual system as an in vivo model to examine the consequence of increased MeCP2 expression during the morphological maturation of individual central neurons in an otherwise intact brain. Single-cell overexpression of wild-type human MeCP2 was combined with time-lapse confocal microscopy imaging to study dynamic mechanisms by which MeCP2 influences tectal neuron dendritic arborization. Analysis of neurons co-expressing DsRed2 demonstrates that MeCP2 overexpression specifically interfered with dendritic elaboration, decreasing the rates of branch addition and elimination over a 48 hour observation period. Moreover, dynamic analysis of neurons co-expressing wt-hMeCP2 and PSD95-GFP revealed that even though neurons expressing wt-hMeCP2 possessed significantly fewer dendrites and simpler morphologies than control neurons at the same developmental stage, postsynaptic site density in wt-hMeCP2-expressing neurons was similar to controls and increased at a rate higher than controls. Together, our in vivo studies support an early, cell-autonomous role for MeCP2 during the morphological differentiation of neurons and indicate that perturbations in MeCP2 gene dosage result in deficits in dendritic arborization that can be compensated, at least in part, by synaptic connectivity changes

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

<p>Abstract</p> <p>Background</p> <p>Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of <it>k</it>-partite graphs. These graphs contain <it>k </it>different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.</p> <p>Results</p> <p>Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a <it>k</it>-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted <it>k</it>-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.</p> <p>Conclusions</p> <p>In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy <it>k</it>-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.</p

Biogenic amines and their metabolites are differentially affected in the Mecp2-deficient mouse brain

International audienceBACKGROUND: Rett syndrome (RTT, MIM #312750) is a severe neurological disorder caused by mutations in the X-linked methyl-CpG binding protein 2 (MECP2) gene. Female patients are affected with an incidence of 1/15000 live births and develop normally from birth to 6-18 months of age before the onset of deficits in autonomic, cognitive, motor functions (stereotypic hand movements, impaired locomotion) and autistic features. Studies on Mecp2 mouse models, and specifically null mice, revealed morphological and functional alterations of neurons. Several functions that are regulated by bioaminergic nuclei or peripheral ganglia are impaired in the absence of Mecp2. RESULTS: Using high performance liquid chromatography, combined with electrochemical detection (HPLC/EC) we found that Mecp2(-/y) mice exhibit an alteration of DA metabolism in the ponto-bulbar region at 5 weeks followed by a more global alteration of monoamines when the disease progresses (8 weeks). Hypothalamic measurements suggest biphasic disturbances of norepinephrine and serotonin at pathology onset (5 weeks) that were found stabilized later on (8 weeks). Interestingly, the postnatal nigrostriatal dopaminergic deficit identified previously does not parallel the reduction of the other neurotransmitters investigated. Finally, dosage in cortical samples do not suggest modification in the monoaminergic content respectively at 5 and 8 weeks of age. CONCLUSIONS: We have identified that the level of catecholamines and serotonin is differentially affected in Mecp2(-/y) brain areas in a time-dependent fashion

Role of MeCP2, DNA methylation, and HDACs in regulating synapse function

Over the past several years there has been intense effort to delineate the role of epigenetic factors, including methyl-CpG-binding protein 2, histone deacetylases, and DNA methyltransferases, in synaptic function. Studies from our group as well as others have shown that these key epigenetic mechanisms are critical regulators of synapse formation, maturation, as well as function. Although most studies have identified selective deficits in excitatory neurotransmission, the latest work has also uncovered deficits in inhibitory neurotransmission as well. Despite the rapid pace of advances, the exact synaptic mechanisms and gene targets that mediate these effects on neurotransmission remain unclear. Nevertheless, these findings not only open new avenues for understanding neuronal circuit abnormalities associated with neurodevelopmental disorders but also elucidate potential targets for addressing the pathophysiology of several intractable neuropsychiatric disorders

Human Intelligence and Polymorphisms in the DNA Methyltransferase Genes Involved in Epigenetic Marking

Epigenetic mechanisms have been implicated in syndromes associated with mental impairment but little is known about the role of epigenetics in determining the normal variation in human intelligence. We measured polymorphisms in four DNA methyltransferases (DNMT1, DNMT3A, DNMT3B and DNMT3L) involved in epigenetic marking and related these to childhood and adult general intelligence in a population (n = 1542) consisting of two Scottish cohorts born in 1936 and residing in Lothian (n = 1075) or Aberdeen (n = 467). All subjects had taken the same test of intelligence at age 11yrs. The Lothian cohort took the test again at age 70yrs. The minor T allele of DNMT3L SNP 11330C>T (rs7354779) allele was associated with a higher standardised childhood intelligence score; greatest effect in the dominant analysis but also significant in the additive model (coefficient = 1.40additive; 95%CI 0.22,2.59; p = 0.020 and 1.99dominant; 95%CI 0.55,3.43; p = 0.007). The DNMT3L C allele was associated with an increased risk of being below average intelligence (OR 1.25additive; 95%CI 1.05,1.51; p = 0.011 and OR 1.37dominant; 95%CI 1.11,1.68; p = 0.003), and being in the lowest 40th (padditive = 0.009; pdominant = 0.002) and lowest 30th (padditive = 0.004; pdominant = 0.002) centiles for intelligence. After Bonferroni correction for the number variants tested the link between DNMT3L 11330C>T and childhood intelligence remained significant by linear regression and centile analysis; only the additive regression model was borderline significant. Adult intelligence was similarly linked to the DNMT3L variant but this analysis was limited by the numbers studied and nature of the test and the association was not significant after Bonferroni correction. We believe that the role of epigenetics in the normal variation in human intelligence merits further study and that this novel finding should be tested in other cohorts

Expression Profiling of Autism Candidate Genes during Human Brain Development Implicates Central Immune Signaling Pathways

The Autism Spectrum Disorders (ASD) represent a clinically heterogeneous set of conditions with strong hereditary components. Despite substantial efforts to uncover the genetic basis of ASD, the genomic etiology appears complex and a clear understanding of the molecular mechanisms underlying Autism remains elusive. We hypothesized that focusing gene interaction networks on ASD-implicated genes that are highly expressed in the developing brain may reveal core mechanisms that are otherwise obscured by the genomic heterogeneity of the disorder. Here we report an in silico study of the gene expression profile from ASD-implicated genes in the unaffected developing human brain. By implementing a biologically relevant approach, we identified a subset of highly expressed ASD-candidate genes from which interactome networks were derived. Strikingly, immune signaling through NFκB, Tnf, and Jnk was central to ASD networks at multiple levels of our analysis, and cell-type specific expression suggested glia—in addition to neurons—deserve consideration. This work provides integrated genomic evidence that ASD-implicated genes may converge on central cytokine signaling pathways

Blood transcriptomics of drug-na\uefve sporadic Parkinson's disease patients

BACKGROUND: Parkinson's disease (PD) is a chronic progressive neurodegenerative disorder that is clinically defined in terms of motor symptoms. These are preceded by prodromal non-motor manifestations that prove the systemic nature of the disease. Identifying genes and pathways altered in living patients provide new information on the diagnosis and pathogenesis of sporadic PD. METHODS: Changes in gene expression in the blood of 40 sporadic PD patients and 20 healthy controls ("Discovery set") were analyzed by taking advantage of the Affymetrix platform. Patients were at the onset of motor symptoms and before initiating any pharmacological treatment. Data analysis was performed by applying Ranking-Principal Component Analysis, PUMA and Significance Analysis of Microarrays. Functional annotations were assigned using GO, DAVID, GSEA to unveil significant enriched biological processes in the differentially expressed genes. The expressions of selected genes were validated using RT-qPCR and samples from an independent cohort of 12 patients and controls ("Validation set"). RESULTS: Gene expression profiling of blood samples discriminates PD patients from healthy controls and identifies differentially expressed genes in blood. The majority of these are also present in dopaminergic neurons of the Substantia Nigra, the key site of neurodegeneration. Together with neuronal apoptosis, lymphocyte activation and mitochondrial dysfunction, already found in previous analysis of PD blood and post-mortem brains, we unveiled transcriptome changes enriched in biological terms related to epigenetic modifications including chromatin remodeling and methylation. Candidate transcripts as CBX5, TCF3, MAN1C1 and DOCK10 were validated by RT-qPCR. CONCLUSIONS: Our data support the use of blood transcriptomics to study neurodegenerative diseases. It identifies changes in crucial components of chromatin remodeling and methylation machineries as early events in sporadic PD suggesting epigenetics as target for therapeutic intervention