The TAO-Gen Algorithm for Identifying Gene Interaction Networks with Application to SOS Repair in E. coli

One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression

G1(M) no bekutoruba to kori A

制度:新 ; 文部省報告番号:乙1532号 ; 学位の種類:博士(理学) ; 授与年月日:2000-03-02 ; 早大学位記番号:新2995 ; 理工学図書館請求番号:2493早稲田大

Gene regulatory network analysis defines transcriptome landscape with alternative splicing of human umbilical vein endothelial cells during replicative senescence

Background Endothelial cell senescence is the state of permanent cell cycle arrest and plays a critical role in the pathogenesis of age-related diseases. However, a comprehensive understanding of the gene regulatory network, including genome-wide alternative splicing machinery, involved in endothelial cell senescence is lacking. Results We thoroughly described the transcriptome landscape of replicative senescent human umbilical vein endothelial cells. Genes with high connectivity showing a monotonic expression increase or decrease with the culture period were defined as hub genes in the co-expression network. Computational network analysis of these genes led to the identification of canonical and non-canonical senescence pathways, such as E2F and SIRT2 signaling, which were down-regulated in lipid metabolism, and chromosome organization processes pathways. Additionally, we showed that endothelial cell senescence involves alternative splicing. Importantly, the first and last exon types of splicing, as observed in FLT1 and ACACA, were preferentially altered among the alternatively spliced genes during endothelial senescence. We further identified novel microexons in PRUNE2 and PSAP, each containing 9 nt, which were altered within the specific domain during endothelial senescence. Conclusions These findings unveil the comprehensive transcriptome pathway and novel signaling regulated by RNA processing, including gene expression and splicing, in replicative endothelial senescence.Medicine, Faculty ofNon UBCPathology and Laboratory Medicine, Department ofReviewedFacultyResearcherOthe

Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing

The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children’s environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds