Differential gene expression after gamma radiation in bis-benzimidazole-treated human glioblastoma using microarray analysis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Tandon Vibha
PROVIDER: S-ECPF-GEOD-30043 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Radiation-induced DNA damage initiates a complex series of overlapping responses responsible for the maintenance of genome integrity. This study reports the expression analysis in response to DNA minor groove binding ligand (DMA-5-(4-methylpiperazin-1-yl)-2-[2’-(3,4-dimethoxyphenyl)-5`-benzimidazolyl] benzimidazole, an analogue of Hoechst 33342), with an emphasis on its ability to afford better protection in cells exposed to ionizing radiation.
2011-08-01 | GSE30043 | GEO
Project description:Radiation-induced DNA damage initiates a complex series of overlapping responses responsible for the maintenance of genome integrity. This study reports the expression analysis in response to DNA minor groove binding ligand (DMA-5-(4-methylpiperazin-1-yl)-2-[2’-(3,4-dimethoxyphenyl)-5`-benzimidazolyl] benzimidazole, an analogue of Hoechst 33342), with an emphasis on its ability to afford better protection in cells exposed to ionizing radiation. Four different types of samples were employed in the analysis: Control (untreated) cells, 50µM ligand-treated cells, 8.5Gy radiation-treated cells, and cells treated with 50µM ligand one hour prior to irradiation.
2011-07-31 | E-GEOD-30043 | biostudies-arrayexpress
Project description:With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations.We develop methods to retrieve gene expression experiments that differentially express the same transcriptional programs as a query experiment. Avoiding thresholds, we generate differential expression profiles that include a score for each gene measured in an experiment. We use existing and novel dimension reduction and correlation measures to rank relevant experiments in an entirely data-driven manner, allowing emergent features of the data to drive the results. A combination of matrix decomposition and p-weighted Pearson correlation proves the most suitable for comparing differential expression profiles. We apply this method to index all GEO DataSets, and demonstrate the utility of our approach by identifying pathways and conditions relevant to transcription factors Nanog and FoxO3.Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets.
| S-EPMC3022631 | biostudies-literature
Project description:BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.ResultsIn this study, we propose a novel differential expression and feature selection method-GEOlimma-which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.ConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.
| S-EPMC7860207 | biostudies-literature
Project description:BackgroundTypical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.ResultsWe use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes.ConclusionThe methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.
| S-EPMC3045302 | biostudies-literature
Project description:The macula is a unique and important region in the primate retina that achieves high resolution and color vision in the central visual field. We recently reported data obtained from microarray analysis of gene expression in the macula of the human fetal retina (Kozulin et al., Mol Vis 15:45-59, 1). In this paper, we describe the preliminary analyses undertaken to visualize differences and verify comparability of the replicates used in that study, report the differential expression of other gene families obtained from the analysis, and show the reproducibility of our findings in several gene families by quantitative real-time PCR.
| S-EPMC2816828 | biostudies-literature
Project description:In the title centrosymmetric mononuclear copper(II) compound, [Cu(C(14)H(9)O(3))(2)(C(7)H(6)N(2))(2)], the central Cu(II) ion is coordinated by two N atoms from two symmetry-related benzimidazole (bim) ligands and two O atoms from two symmetry-related 2-benzoyl-benzoate (2-byba) ligands in a square-planar geometry. The mol-ecules are linked into chains running along the b axis by N-H⋯O hydrogen bonds and the chains are cross-linked by C-H⋯π inter-actions.
| S-EPMC2969713 | biostudies-literature
Project description:Controlling the polymerization of supramolecular self-assembly through external stimuli holds great potential for the development of responsive soft materials and manipulation at the nanoscale. Vinyl esters of bis(leu or val)fumaramide (1a and 2a) have been found to be gelators of various organic solvents and were applied in this investigation of the influence of organogelators' self-assembly on solid-state polymerization induced by gamma and ultraviolet irradiation. Here, we report our investigation into the influences of self-assemblies of bis(amino acid vinyl ester)fumaramides on gamma-ray- and ultraviolet-induced polymerization. The gelator molecules self-assembled by non-covalent interactions, mainly through hydrogen bonds between the amide group (CONH) and the carboxyl group (COO), thus forming a gel network. NMR and FTIR spectroscopy were used to investigate and characterize supramolecular gels. TEM and SEM microscopy were used to investigate the morphology of gels and polymers. Morphology studies showed that the gels contained a filamentous structure of nanometer dimensions that was exhaustive in a three-dimensional network. The prepared derivatives contained reactive alkyl groups suitable for carrying out the polymerization reaction initiated by gamma or ultraviolet radiation in the supramolecular aggregates of selected gels. It was found that the polymerization reaction occurred only in the network of the gel and was dependent on the structure of aggregates or the proximity and orientation of double bonds in the gel network. Polymers were formed by the gels exposure to gamma and ultraviolet radiation in toluene, and water/DMF gels with transcripts of their gel structure into polymers. The polymeric material was able to immobilize various solvents by swelling. Furthermore, methyl esters of bis(leu and val)fumaramide (1b and 2b) were synthesized; these compounds showed no gelling properties, and the crystal structure of the valine derivative 2b was determined.
| S-EPMC8747669 | biostudies-literature
Project description:Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchies. Here we use high resolution mapping of copy number eQTLs (ceQTLs) in a mouse-hamster radiation hybrid (RH) panel to construct directed genetic networks in the mammalian cell. The RH network covering 20,145 mouse genes had significant overlap with, and similar topological structures to, existing biological networks. Upregulated edges in the RH network had significantly more overlap than downregulated. This suggests repressive relationships between genes are missed by existing approaches, perhaps because the corresponding proteins are not present in the cell at the same time and therefore unlikely to interact. Gene essentiality was positively correlated with connectivity and betweenness centrality in the RH network, strengthening the centrality-lethality principle in mammals. Consistent with their regulatory role, transcription factors had significantly more outgoing edges (regulating) than incoming (regulated) in the RH network, a feature hidden by conventional undirected networks. Directed RH genetic networks thus showed concordance with pre-existing networks while also yielding information inaccessible to current undirected approaches.
| S-EPMC2690838 | biostudies-literature
Project description:BackgroundPrevious differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.ResultsdCoxS is a two-step analysis method. In each condition, dCoxS measures the interaction score (IS), which represents the expression similarity between two gene sets using Renyi relative entropy. When estimating the relative entropy, multivariate kernel density estimation was used to model gene-gene correlation structure. Statistical tests for the conditional difference between the ISs determined the significance of differential coexpression of the gene set pair. Simulation studies supported that the IS is a representative measure of similarity between gene expression matrices. Single gene coexpression analysis of two publicly available microarray datasets detected no significant results. However, the dCoxS analysis of the datasets revealed differentially coexpressed gene set pairs related to the biological conditions of the datasets.ConclusiondCoxS identified differentially coexpressed gene set pairs not found by single gene analysis. The results indicate that set-wise differential coexpression analysis is useful for understanding biological processes induced by conditional changes.
| S-EPMC2679020 | biostudies-literature