Compendium Of Expression Profiles Research Articles

Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles

BackgroundNeural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer's disease (AD). While much progress has been made in understanding neural stem cell function, a precise description of the molecular mechanisms regulating neural stem cells is not yet established. This lack of knowledge is a major barrier holding back the discovery of therapeutic uses of neural stem cells. In this paper, the regulatory mechanism of mouse neural stem cell (NSC) differentiation by tmem59 is explored on the genome-level.ResultsWe identified regulators of tmem59 during the differentiation of mouse NSCs from a compendium of expression profiles. Based on the microarray experiment, we developed the parallelized SWNI algorithm to reconstruct gene regulatory networks of mouse neural stem cells. From the inferred tmem59 related gene network including 36 genes, pou6f1 was identified to regulate tmem59 significantly and might play an important role in the differentiation of NSCs in mouse brain. There are four pathways shown in the gene network, indicating that tmem59 locates in the downstream of the signalling pathway. The real-time RT-PCR results shown that the over-expression of pou6f1 could significantly up-regulate tmem59 expression in C17.2 NSC line. 16 out of 36 predicted genes in our constructed network have been reported to be AD-related, including Ace, aqp1, arrdc3, cd14, cd59a, cds1, cldn1, cox8b, defb11, folr1, gdi2, mmp3, mgp, myrip, Ripk4, rnd3, and sncg. The localization of tmem59 related genes and functional-related gene groups based on the Gene Ontology (GO) annotation was also identified.ConclusionsOur findings suggest that the expression of tmem59 is an important factor contributing to AD. The parallelized SWNI algorithm increased the efficiency of network reconstruction significantly. This study enables us to highlight novel genes that may be involved in NSC differentiation and provides a shortcut to identifying genes for AD.

A bioinformatic implementation of mutual information as a distance measure for identification of clusters of variables

The size of data sets produced in genetic experiments is steadily increasing. Very often there are many more variables than observations, leading to the so-called ``large $p$, small $n$" problem. For such data, clustering and distance based procedures are useful tools for identifying groups of variables associated with outcomes of interest. We develop a novel approach using mutual information as a measure of distance (here dependency) between probability distributions that is valid for comparisons between pairs of variables that are both continuous, both discrete, or one of each. This gives an overall information matrix to be used as a distance matrix in clustering procedures and to define a so-called weighted network of associations between variables. We present computational aspects of implementing our procedures in R. References T. M. Cover and J. A. Thomas. Elements of information theory. Wiley, 2006. doi:10.1002/0471200611. Z. Dawy, B. Goebel, J. Hagenauer, C. Andreoli, T. Meitinger, and J. C. Mueller. Gene mapping and marker clustering using Shannon's mutual information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 3(1):47--56, 2006. doi:10.1109/TCBB.2006.9. J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins, and T. S. Gardner. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol, 5(1):e8, 2007. doi:10.1371/journal.pbio.0050008. T. F. Fuller, A. Ghazalpour, J. E. Aten, T. A. Drake, A. J. Lusis, and S. Horvath. Weighted gene coexpression network analysis strategies applied to mouse weight. Mammalian Genome, 18(6):463--472, 2007. doi:10.1007/s00335-007-9043-3. A. Ghazalpour, S. Doss, B. Zhang, S. Wang, C. Plaisier, R. Castellanos, A. Brozell, E. E. Schadt, T. A. Drake, A. J. Lusis, et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet, 2(8):e130, 2006. doi:10.1371/journal.pgen.0020130. Hung T. Nguyen. On modeling of linguistic information using random sets. Information Sciences, 34(3):265 -- 274, 1984. doi:10.1016/0020-0255(84)90052-5. P. Qiu, A. J. Gentles, and S. K. Plevritis. Fast calculation of pairwise mutual information for gene regulatory network reconstruction. Computer Methods and Programs in Biomedicine, 94(2):177--180, 2009. doi:10.1016/0020-0255(84)90052-5. S. J. Sheather and M. C. Jones. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B (Methodological), pages 683--690, 1991. M. P. Wand and M. C. Jones. Kernel smoothing. Chapman and Hall/CRC, 1995. B. Zhang and S. Horvath. A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1):1128, 2005. doi:10.2202/1544-6115.1128.

Pathways of the Heart

Disease heterogeneity presents a formidable challenge for clinical medicine. We are unlikely to develop successful, targeted treatments unless diagnostic schemes begin to reflect the biological complexity underlying superficially similar disease phenotypes. To address heterogeneity, there is an increasing recognition that realistic disease models need to be built on a foundation of quantitative molecular information.1 Such a “systems medicine” approach should ultimately allow classification into biologically more homogeneous groups, with similar prognosis and treatment response. Article see p 371 Oncology has been among the first specialties to embrace molecular profiling to improve clinical decision making. Today, a wealth of expression profiling information exists for tumors, and early efforts are being made to classify patients into likely treatment responders for specific chemotherapeutics.2 The investigation of complex multisystem pathologies, such as cardiovascular disease, has been slower to incorporate molecular profiling, in part, because of the involvement of multiple tissues, many of which are not readily accessible. Moreover, cardiovascular disease, unlike cancer, is not clonal in origin, making experimental analyses more challenging. It is clear, however, that broadly defined diseases such as the cardiomyopathies are the product of diverse genetic and environmental agents3; one can expect that quantitative molecular profiling will shed light on distinct, pathological activities underlying these conditions, thus allowing more meaningful classification schemes beyond those that are solely based on anatomic or hemodynamic considerations. The article by Barth et al4 in this issue of Circulation: Cardiovascular Genetics represents such a broadened search for molecular correlates of cardiovascular disease. The authors focus on the problem of heart failure with mechanical dyssynchrony (DHF) and its treatment by cardiac resynchronization therapy (CRT). Clinically, CRT has been a remarkable success, with significant gains in quality of life and mortality.5 Multiple studies have investigated the physiological consequences of dyssynchrony and …

Alternative Pathway Approach for Automating Analysis and Validation of Cell Perturbation Networks and Design of Perturbation Experiments

Cell perturbation data are a very important resource to analyze and reconstruct cell-signaling networks. To facilitate the utilization of this type of data and enable large-scale and automated network reconstruction effort, we have already developed a data structure for storing cell perturbation results and deducing perturbation networks (CellFrame). For automating network analysis, we here propose a computational method called the "alternative pathway approach" (ALPA) in this work. This method can validate the signaling networks with conditional perturbation data extracted from published experiments, and can suggest additional tests to improve the network. It searches the alternative pathway space between all pairs of nodes, constructs pathnets (set of pathways between two nodes) and validates the network edges using conditional perturbation. For pathnets without conditional data, experiments with the fewest number of perturbations or the most parsimonious are designed. For pathnets without experimentally derived or detected pair-wise interactions, ALPA can predict the potential effects or propose additional pathways to expand the existing network. We have tested the ALPA method on the TNFalpha-MAPK signaling cascade; the reconstructed network is consistent with the consensus model. We also used the ALPA algorithm to analyze a yeast gene perturbation network, using the data from the Rosetta compendium of expression profiles, which demonstrates that it can be also used for large-scale analysis. We compared the performance of the ALPA approach with Boolean and Bayesian network algorithms for their efficiency and accuracy, respectively, in network construction.

Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.

Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

A synthetic gene network for tuning protein degradation in Saccharomyces cerevisiae

Protein decay rates are regulated by degradation machinery that clears unnecessary housekeeping proteins and maintains appropriate dynamic resolution for transcriptional regulators. Turnover rates are also crucial for fluorescence reporters that must strike a balance between sufficient fluorescence for signal detection and temporal resolution for tracking dynamic responses. Here, we use components of the Escherichia coli degradation machinery to construct a Saccharomyces cerevisiae strain that allows for tunable degradation of a tagged protein. Using a microfluidic platform tailored for single-cell fluorescence measurements, we monitor protein decay rates after repression using an ssrA-tagged fluorescent reporter. We observe a half-life ranging from 91 to 22 min, depending on the level of activation of the degradation genes. Computational modeling of the underlying set of enzymatic reactions leads to GFP decay curves that are in excellent agreement with the observations, implying that degradation is governed by Michaelis–Menten-type interactions. In addition to providing a reporter with tunable dynamic resolution, our findings set the stage for explorations of the effect of protein degradation on gene regulatory and signalling pathways.

Integrating Expression Profiling Into Drug Discovery

Toxicity and lack of efficacy account for about 60% of drug pipeline attrition, and early stage amelioration of these issues can increase the probability of success for new drug candidates. To achieve this effectively requires knowledge of target and off-target effects, identification of biomarkers, and an understanding of genotypic/phenotypic diversity within patient groups. Analysis of gene expression profiles obtained from target tissues reveals distinct exposure-related characteristics as defined by distinct transcriptional signatures. Using a compendium of expression profiles, compound-specific effects for new chemicals can be compared to responses for known and characterized compounds to help identify target effects and toxic liabilities even when gene annotations are incomplete or inconclusive. Further, where annotations exist discrete gene responses can provide specific mechanistic insights through the identification of response pathways, including those controlled by both the intended therapeutic target and untoward off-target effects. By examining human gene expression, discrete preclinical gene changes can also help identify putative transcriptional biomarkers that may translate into clinically accessible biomarkers. Thus, expression profiling may ultimately drive lower failure rates at all stages of drug development allowing faster pipeline progression, as well as producing successful therapies for patient subgroups.