Genetic regulatory network analysis for app based on genetical genomics approach.
ABSTRACT A number of studies have shown that amyloid precursor protein (App) plays a critical role in Alzheimer's disease (AD); however, little is known about the genetic regulatory network. In this study, the authors combined array analysis and quantitative trait loci (QTL) mapping to characterize the genetic variation and genetic regulatory network for App using hippocampus of BXD recombinant inbred (RI) mice. The variation in expression level of App is conspicuous across the 78 BXD RI strains. Moreover, the expression level of App is significantly higher in DBA/2J than the level in C57BL/6J (p < .001). Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis has further confirmed the significant difference between the two parental strains C57BL/6J and DBA/2J. The authors performed an interval mapping for App gene expression and found that it is cis regulated with highly significant likelihood-ratio statistic (LRS) score (LRS = 19; p < .05). Four SNPs and two InDels (insertions or deletions) were identified in the promoter, and one of the SNPs is located in the pax2 motif. Genetic regulatory network analysis showed that App coregulated with many AD-related genes, including Gsk3b, Falz, Mef2a, Tlk2, Rtn, and Prkca. The genetical genomics approach demonstrates the importance and the potential power of the expression quantitative trait loci (eQTL) studies in identifying regulatory network that contribute to complex traits, such as AD.
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ABSTRACT: Alzheimer's disease (AD) is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF) activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA) algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA), which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.Computational and Mathematical Methods in Medicine 06/2014; 2014:891761. DOI:10.1155/2014/891761 · 1.02 Impact Factor
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ABSTRACT: Mouse embryonic stem (ES) cells are derived from the inner cell mass of blastocyst stage embryos and are used primarily for the creation of genetically engineered strains through gene targeting. While some inbred strains of mice are permissive to the derivation of embryonic stem cell lines and are therefore easily engineered, others are nonpermissive or recalcitrant. Genetic engineering of recalcitrant strain backgrounds requires gene targeting in a permissive background followed by extensive backcrossing of the engineered allele into the desired strain background. The inbred mouse strain DBA/2J is a recalcitrant strain that is used as a model of many human diseases, including glaucoma, deafness and schizophrenia. Here, we describe the generation of germ-line competent ES cell lines derived from DBA/2J mice. We also demonstrate the utility of DBA/2J ES cells with the creation of conditional knockout allele for Endothelin-2 (Edn2) directly on the DBA/2J strain background.PLoS ONE 11/2012; 7(11):e50081. DOI:10.1371/journal.pone.0050081 · 3.53 Impact Factor
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ABSTRACT: Single-nucleotide polymorphisms (SNPs) contribute to the between-individual expression variation of many genes. A regulatory (trait-associated) SNP is usually located near or within a (host) gene, possibly influencing the gene's transcription or/and post-transcriptional modification. But its targets may also include genes that are physically farther away from it. A heuristic explanation of such multiple-target interferences is that the host gene transfers the SNP genotypic effects to the distant gene(s) by a transcriptional or signaling cascade. These connections between the host genes (regulators) and the distant genes (targets) make the genetic analysis of gene expression traits a promising approach for identifying unknown regulatory relationships. In this study, through a mixed model analysis of multi-source digital expression profiling for 140 human lymphocyte cell lines (LCLs) and the genotypes distributed by the international HapMap project, we identified 45 thousands of potential SNP-induced regulatory relationships among genes (the significance level for the underlying associations between expression traits and SNP genotypes was set at FDR < 0.01). We grouped the identified relationships into four classes (paradigms) according to the two different mechanisms by which the regulatory SNPs affect their cis- and trans- regulated genes, modifying mRNA level or altering transcript splicing patterns. We further organized the relationships in each class into a set of network modules with the cis- regulated genes as hubs. We found that the target genes in a network module were often characterized by significant functional similarity, and the distributions of the target genes in three out of the four networks roughly resemble a power-law, a typical pattern of gene networks obtained from mutation experiments. By two case studies, we also demonstrated that significant biological insights can be inferred from the identified network modules.PLoS ONE 10/2013; 8(10):e78868. DOI:10.1371/journal.pone.0078868 · 3.53 Impact Factor