Searching across hundreds of databases
The human developmental diseases Cornelia de Lange Syndrome (CdLS) and Roberts Syndrome (RBS) are both caused by mutations in proteins responsible for sister chromatid cohesion. Cohesion is mediated by a multi-subunit complex called cohesin, which is loaded onto chromosomes by NIPBL. Once on chromosomes, cohesin binding is stabilized in S phase upon acetylation by ESCO2. CdLS is caused by heterozygous mutations in NIPBL or cohesin subunits SMC1A and SMC3, and RBS is caused by homozygous mutations in ESCO2. The genetic cause of both CdLS and RBS reside within the chromosome cohesion apparatus, and therefore they are collectively known as "cohesinopathies". However, the two syndromes have distinct phenotypes, with differences not explained by their shared ontology. In this study, we have used the zebrafish model to distinguish between developmental pathways downstream of cohesin itself, or its acetylase ESCO2. Esco2 depleted zebrafish embryos exhibit features that resemble RBS, including mitotic defects, craniofacial abnormalities and limb truncations. A microarray analysis of Esco2-depleted embryos revealed that different subsets of genes are regulated downstream of Esco2 when compared with cohesin subunit Rad21. Genes downstream of Rad21 showed significant enrichment for transcriptional regulators, while Esco2-regulated genes were more likely to be involved the cell cycle or apoptosis. RNA in situ hybridization showed that runx1, which is spatiotemporally regulated by cohesin, is expressed normally in Esco2-depleted embryos. Furthermore, myca, which is downregulated in rad21 mutants, is upregulated in Esco2-depleted embryos. High levels of cell death contributed to the morphology of Esco2-depleted embryos without affecting specific developmental pathways. We propose that cell proliferation defects and apoptosis could be the primary cause of the features of RBS. Our results show that mutations in different elements of the cohesion apparatus have distinct developmental outcomes, and provide insight into why CdLS and RBS are distinct diseases.
Pubmed ID: 21637801 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Non-profit academic organization for research and services in bioinformatics. Provides freely available data from life science experiments, performs basic research in computational biology, and offers user training programme, manages databases of biological data including nucleic acid, protein sequences, and macromolecular structures. Part of EMBL.
View all literature mentionsSoftware that characterizes coexisting subpopulations (SPs) in a tumor using copy number and allele frequencies derived from exome- or whole genome sequencing input data. The model amplifies the statistical power to detect coexisting genotypes, by fully exploiting run-specific tradeoffs between depth of coverage and breadth of coverage. ExPANdS predicts the number of clonal expansions, the size of the resulting SPs in the tumor bulk, the mutations specific to each SP and tumor purity. The main function runExPANdS provides the complete functionality needed to predict coexisting SPs from single nucleotide variations (SNVs) and associated copy numbers. The robustness of the subpopulation predictions by ExPANdS increases with the number of mutations provided. It is recommended that at least 200 mutations are used as an input to obtain stable results.
View all literature mentionsA web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.
View all literature mentionsSoftware package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
