DNA microarray data and contextual analysis of correlation graphs

Estimating Electroencephalograph Network Parameters Using Mutual Information

Statistical parameters that measure strength, integration, and segregation of a multichannel electroencephalograph (EEG) network are evaluated using a similarity measure based on mutual information (MI) between the measured channel data. Compared with the unsigned linear correlation coefficient, MI is more robust to volume conduction and is applicable to nonlinear data. The statistical parameters estimated are node strength, average path length, and clustering coefficient. These parameters provide valuable insights into the brain network of the subject. MI is evaluated using a recently developed procedure based on the Gaussian copula. It is a computationally efficient procedure since estimation of MI is carried out analytically. This procedure is illustrated here for a 30-channel random noise and EEG network. The results are compared with those obtained using the linear correlation coefficient. The results show improvements by using MI to estimate the network properties.

FUNCTION PREDICTION OF UNCHARACTERIZED PROTEINS

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.

Gene regulation in the rat prefrontal cortex after learning with or without cholinergic insult

The prefrontal cortex is essential for a wide variety of higher functions, including attention and memory. Cholinergic neurons are thought to be of prime importance in the modulation of these processes. Degeneration of forebrain cholinergic neurons has been linked to several neurological disorders. The present study was designed to identify genes and networks in rat prefrontal cortex that are associated with learning and cholinergic-loss-memory deficit. Affymetrix microarray technology was used to screen gene expression changes in rats submitted or not to 192 IgG-saporin immunolesion of cholinergic basal forebrain and trained in spatial/object novelty tasks. Results showed learning processes were associated with significant expression of genes, which were organized in several clusters of highly correlated genes and would be involved in biological processes such as intracellular signaling process, transcription regulation, and filament organization and axon guidance. Memory loss following cortical cholinergic deafferentation was associated with significant expression of genes belonging to only one clearly delineated cluster and would be involved in biological processes related to cytoskeleton organization and proliferation, and glial and vascular remodeling, i.e., in processes associated with brain repair after injury.

Gene expression profile in rat hippocampus with and without memory deficit

The cholinergic neuronal system, through its projections to the hippocampus, plays an important role in learning and memory. The aim of the study was to identify genes and networks in rat hippocampus with and without memory deficit. Genome-scale screening was used to analyze gene expression changes in rats submitted or not to intraparenchymal injection of 192 IgG-saporin and trained in spatial/object novelty tasks. Results showed learning processes were associated with significant expression of genes that could be grouped into several clusters of similar expression profiles and that are involved in biological functions, namely lipid metabolism, signal transduction, protein metabolism and modification, and transcription regulation. Memory loss following hippocampal cholinergic deafferentation was associated with significant expression of genes that did not show similar cluster organization. Only one cluster of genes could be identified; it included genes that would be involved in tissue remodeling. More important, most of the genes significantly altered in lesioned rats were down-regulated.

Alterations in gene expression profiles correlated with cisplatin cytotoxicity in the glioma U343 cell line

Gliomas are the most common tumors in the central nervous system, the average survival time of patients with glioblastoma multiforme being about 1 year from diagnosis, in spite of harsh therapy. Aiming to study the transcriptional profiles displayed by glioma cells undergoing cisplatin treatment, gene expression analysis was performed by the cDNA microarray method. Cell survival and apoptosis induction following treatment were also evaluated. Drug concentrations of 12.5 to 300 μM caused a pronounced reduction in cell survival rates five days after treatment, whereas concentrations higher than 25 μM were effective in reducing the survival rates to ~1%. However, the maximum apoptosis frequency was 20.4% for 25 μM cisplatin in cells analyzed at 72 h, indicating that apoptosis is not the only kind of cell death induced by cisplatin. An analysis of gene expression revealed 67 significantly (FDR < 0.05) modulated genes: 29 of which down- and 38 up-regulated. These genes belong to several classes (metabolism, protein localization, cell proliferation, apoptosis, adhesion, stress response, cell cycle and DNA repair) that may represent several affected cell processes under the influence of cisplatin treatment. The expression pattern of three genes (RHOA, LIMK2 and TIMP2) was confirmed by the real time PCR method.

Identifying gene interaction enrichment for gene expression data

Gene set analysis allows the inclusion of knowledge from established gene sets, such as gene pathways, and potentially improves the power of detecting differentially expressed genes. However, conventional methods of gene set analysis focus on gene marginal effects in a gene set, and ignore gene interactions which may contribute to complex human diseases. In this study, we propose a method of gene interaction enrichment analysis, which incorporates knowledge of predefined gene sets (e.g. gene pathways) to identify enriched gene interaction effects on a phenotype of interest. In our proposed method, we also discuss the reduction of irrelevant genes and the extraction of a core set of gene interactions for an identified gene set, which contribute to the statistical variation of a phenotype of interest. The utility of our method is demonstrated through analyses on two publicly available microarray datasets. The results show that our method can identify gene sets that show strong gene interaction enrichments. The enriched gene interactions identified by our method may provide clues to new gene regulation mechanisms related to the studied phenotypes. In summary, our method offers a powerful tool for researchers to exhaustively examine the large numbers of gene interactions associated with complex human diseases, and can be a useful complement to classical gene set analyses which only considers single genes in a gene set.

Transcriptional changes in U343 MG-a glioblastoma cell line exposed to ionizing radiation

Glioblastoma multiforme (GBM) is a highly invasive and radioresistant brain tumor. Aiming to study how glioma cells respond to gamma-rays in terms of biological processes involved in cellular responses, we performed experiments at cellular context and gene expression analysis in U343-MG-a GBM cells irradiated with 1 Gy and collected at 6 h post-irradiation. The survival rate was approximately 61% for 1 Gy and was completely reduced at 16 Gy. By performing the microarray technique, 859 cDNA clones were analyzed. The Significance Analysis of Microarray algorithm indicated 196 significant expressed genes (false discovery rate (FDR) = 0.42%): 67 down-regulated and 97 up-regulated genes, which belong to several classes: metabolism, adhesion/cytoskeleton, signal transduction, cell cycle/apoptosis, membrane transport, DNA repair/DNA damage signaling, transcription factor, intracellular signaling, and RNA processing. Differential expression patterns of five selected genes (HSPA9B, INPP5A, PIP5K1A, FANCG, and TPP2) observed by the microarray analysis were further confirmed by the quantitative real time RT-PCR method, which demonstrated an up-regulation status of those genes. These results indicate a broad spectrum of biological processes (which may reflect the radio-resistance of U343 cells) that were altered in irradiated glioma cells, so as to guarantee cell survival.

Microarray-based gene set analysis: a comparison of current methods

Background

The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.

Results

Each of six gene set analysis methods was applied to both simulated and publicly available microarray data sets. Overall, the various methodologies were all found to be better at detecting gene sets that moved from non-active (i.e., genes not expressed) to active states (or vice versa), rather than those that simply changed their level of activity. Methods which incorporate correlation structures were found to provide increased ability to detect altered gene sets in some settings.

Conclusion

Based on the results obtained through the analysis of simulated data, it is clear that the performance of gene set analysis methods is strongly influenced by the features of the data set in question, and that methods which incorporate correlation structures into the analysis process tend to achieve better performance, relative to methods which rely on univariate test statistics.

ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis

Publicly available database of co-expressed gene sets would be a valuable tool for a wide variety of experimental designs, including targeting of genes for functional identification or for regulatory investigation. Here, we report the construction of an Arabidopsis thaliana trans-factor and cis-element prediction database (ATTED-II) that provides co-regulated gene relationships based on co-expressed genes deduced from microarray data and the predicted cis elements. ATTED-II () includes the following features: (i) lists and networks of co-expressed genes calculated from 58 publicly available experimental series, which are composed of 1388 GeneChip data in A.thaliana; (ii) prediction of cis-regulatory elements in the 200 bp region upstream of the transcription start site to predict co-regulated genes amongst the co-expressed genes; and (iii) visual representation of expression patterns for individual genes. ATTED-II can thus help researchers to clarify the function and regulation of particular genes and gene networks.

Computational, integrative, and comparative methods for the elucidation of genetic coexpression networks

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively coregulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for coregulation is detected through the use of quantitative trait locus mapping.

Applications of DNA microarrays in biology

DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included.

Clustering proteins from interaction networks for the prediction of cellular functions

Background

Developing reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes.

Results

Applied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins.

Conclusion

We believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions.

Connectivity distribution of spatial networks

We study spatial networks constructed by randomly placing nodes on a manifold and joining two nodes with an edge whenever their distance is less than a certain cutoff. We derive the general expression for the connectivity distribution of such networks as a functional of the distribution of the nodes. We show that for regular spatial densities, the corresponding spatial network has a connectivity distribution decreasing faster than an exponential. In contrast, we also show that scale-free networks with a power law decreasing connectivity distribution are obtained when a certain information measure of the node distribution (integral of higher powers of the distribution) diverges. We illustrate our results on a simple example for which we present simulation results. Finally, we speculate on the role played by the limiting case P(k) proportional, variant k(-1) which appears empirically to be relevant to spatial networks of biological origin such as the ones constructed from gene expression data.