Dataset Information

Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

ABSTRACT: The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD), and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.

SUBMITTER: van der Zwaag B

PROVIDER: S-EPMC2683930 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

van der Zwaag Bert B Franke Lude L Poot Martin M Hochstenbach Ron R Spierenburg Henk A HA Vorstman Jacob A S JA van Daalen Emma E de Jonge Maretha V MV Verbeek Nienke E NE Brilstra Eva H EH van 't Slot Ruben R Ophoff Roel A RA van Es Michael A MA Blauw Hylke M HM Veldink Jan H JH Buizer-Voskamp Jacobine E JE Beemer Frits A FA van den Berg Leonard H LH Wijmenga Cisca C van Amstel Hans Kristian Ploos HK van Engeland Herman H Burbach J Peter H JP Staal Wouter G WG

PloS one 20090528 5

The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism s ...[more]

PMID: 19492091

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Project description:Purpose:Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods:Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results:We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion:The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.

Project description:BackgroundWhen plants are subjected to cold stress, they undergo a series of molecular and physiological changes to protect themselves from injury. Indica cultivars can usually withstand only mild cold stress in a relatively short period. Hormone-mediated defence response plays an important role in cold stress. Weighted gene co-expression network analysis (WGCNA) is a very useful tool for studying the correlation between genes, identifying modules with high phenotype correlation, and identifying Hub genes in different modules. Many studies have elucidated the molecular mechanisms of cold tolerance in different plants, but little information about the recovery process after cold stress is available.ResultsTo understand the molecular mechanism of cold tolerance in rice, we performed comprehensive transcriptome analyses during cold treatment and recovery stage in two cultivars of near-isogenic lines (9311 and DC907). Twelve transcriptomes in two rice cultivars were determined. A total of 2509 new genes were predicted by fragment splicing and assembly, and 7506 differentially expressed genes were identified by pairwise comparison. A total of 26 modules were obtained by expression-network analysis, 12 of which were highly correlated with cold stress or recovery treatment. We further identified candidate Hub genes associated with specific modules and analysed their regulatory relationships based on coexpression data. Results showed that various plant-hormone regulatory genes acted together to protect plants from physiological damage under short-term low-temperature stress. We speculated that this may be common in rice. Under long-term cold stress, rice improved the tolerance to low-temperature stress by promoting autophagy, sugar synthesis, and metabolism.ConclusionThrough WGCNA analysis at the transcriptome level, we provided a potential regulatory mechanism for the cold stress and recovery of rice cultivars and identified candidate central genes. Our findings provided an important reference for the future cultivation of rice strains with good tolerance.

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Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

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Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

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