36
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Synaptic dysregulation in a human iPS cell model of mental disorders

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Dysregulated neurodevelopment with altered structural and functional connectivity is believed to underlie many neuropsychiatric disorders, and 'a disease of synapses' is the major hypothesis for the biological basis of schizophrenia. Although this hypothesis has gained indirect support from human post-mortem brain analyses and genetic studies, little is known about the pathophysiology of synapses in patient neurons and how susceptibility genes for mental disorders could lead to synaptic deficits in humans. Genetics of most psychiatric disorders are extremely complex due to multiple susceptibility variants with low penetrance and variable phenotypes. Rare, multiply affected, large families in which a single genetic locus is probably responsible for conferring susceptibility have proven invaluable for the study of complex disorders. Here we generated induced pluripotent stem (iPS) cells from four members of a family in which a frameshift mutation of disrupted in schizophrenia 1 (DISC1) co-segregated with major psychiatric disorders and we further produced different isogenic iPS cell lines via gene editing. We showed that mutant DISC1 causes synaptic vesicle release deficits in iPS-cell-derived forebrain neurons. Mutant DISC1 depletes wild-type DISC1 protein and, furthermore, dysregulates expression of many genes related to synapses and psychiatric disorders in human forebrain neurons. Our study reveals that a psychiatric disorder relevant mutation causes synapse deficits and transcriptional dysregulation in human neurons and our findings provide new insight into the molecular and synaptic etiopathology of psychiatric disorders.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: not found

          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

              High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
                Bookmark

                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                November 2014
                August 17 2014
                November 2014
                : 515
                : 7527
                : 414-418
                Article
                10.1038/nature13716
                4501856
                25132547
                46c2b658-d194-45f6-b52f-803351ef9910
                © 2014

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article