Discovering Functional Relationships Between RNA Expression and Chemotherapeutic Susceptibility Using Relevance Networks

Boston Children's Hospital, Boston, Massachusetts, United States
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 11/2000; 97(22):12182-6. DOI: 10.1073/pnas.220392197
Source: PubMed


In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

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    • "The Relevance Network (RelNet) constructs a network in which a pair of random variables X i and Y j is linked by an edge if the mutual information I(X i ,Y j ) is larger than a given threshold [27]. The Context Likelihood of Relatedness (CLR) algorithm derives a score from the empirical distribution of the mutual information for each pair of random variables X i and Y j [28]. "
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    ABSTRACT: Background High-throughput methods for biological measurements generate vast amounts of quantitative data, which necessitate the development of advanced approaches to data analysis to help understand the underlying mechanisms and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology. Results We use an information theoretic approach to reconstruct phosphoprotein-cytokine networks in RAW 264.7 macrophage cells. Cytokines are secreted upon activation of a wide range of regulatory signals transduced by the phosphoprotein network. Identifying these components can help identify regulatory modules responsible for the inflammatory phenotype. The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators. Mutual information provides a measure of statistical dependencies between interacting components. Using the topology of the network derived, we develop a data-driven parsimonious input–output model of the phosphoprotein-cytokine network. Conclusions We demonstrate the applicability of our information theoretic approach to reconstruction of biological networks. For the phosphoprotein-cytokine network, this approach not only captures most of the known signaling components involved in cytokine release but also predicts new signaling components involved in the release of cytokines. The results of this study are important for gaining a clear understanding of macrophage activation during the inflammation process.
    BMC Systems Biology 06/2014; 8(1):77. DOI:10.1186/1752-0509-8-77 · 2.44 Impact Factor
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    • "miRNA relevant networks were created by connecting adipose core miRNAs according to the correlation (R2 ≥ 0.95) of their expression over 24 samples using Relevance Networks tool [77] from the Multiple Array Viewer from Multi Experiment Viewer software (v.4.8) [78]. Prediction of target genes was performed for each core adipose miRNA using TargetScan 6.2 for mammals and customized by species (cow/Bos taurus) ( "
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    ABSTRACT: MicroRNAs (miRNAs) are small non-coding RNAs found to regulate several biological processes including adipogenesis. Understanding adipose tissue regulation is critical for beef cattle as fat is an important determinant of beef quality and nutrient value. This study analyzed the association between genomic context characteristics of miRNAs with their expression and function in bovine adipose tissue. Twenty-four subcutaneous adipose tissue biopsies were obtained from eight British-continental crossbred steers at 3 different time points. Total RNA was extracted and miRNAs were profiled using a miRNA microarray with expression further validated by qRT-PCR. A total of 224 miRNAs were detected of which 155 were expressed in all steers (n = 8), and defined as the core miRNAs of bovine subcutaneous adipose tissue. Core adipose miRNAs varied in terms of genomic location (59.5% intergenic, 38.7% intronic, 1.2% exonic, and 0.6% mirtron), organization (55.5% non-clustered and 44.5% clustered), and conservation (49% highly conserved, 14% conserved and 37% poorly conserved). Clustered miRNAs and highly conserved miRNAs were more highly expressed (p < 0.05) and had more predicted targets than non-clustered or less conserved miRNAs (p < 0.001). A total of 34 miRNAs were coordinately expressed, being part of six identified relevant networks. Two intronic miRNAs (miR-33a and miR-1281) were confirmed to have coordinated expression with their host genes, transcriptional factor SREBF2 and EP300 (a transcriptional co-activator of transcriptional factor C/EBPalpha), respectively which are involved in lipid metabolism, suggesting these miRNAs may also play a role in regulation of bovine lipid metabolism/adipogenesis. Furthermore, a total of 17 bovine specific miRNAs were predicted to be involved in the regulation of energy balance in adipose tissue. These findings improve our understanding on the behavior of miRNAs in the regulation of bovine adipogenesis and fat metabolism as it reveals that miRNA expression patterns and functions are associated with miRNA genomic location, organization and conservation.
    BMC Genomics 02/2014; 15(1):137. DOI:10.1186/1471-2164-15-137 · 3.99 Impact Factor
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    • "is an extension of the relevance network approach [4]. The latter approach has been introduced for gene clustering and successfully applied to infer relationships between RNA expression and chemotherapeutic susceptibility [3]. The relevance networks approach consists of inferring a network in which a pair of genes {X i , X j } are linked by an edge if the mutual information I(X i ; X j ) is larger than a given threshold θ. "
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    ABSTRACT: This chapter introduces the curse of dimensionality, and focuses on widely used variable exploration strategies. The chapter also introduces the information-theoretic framework and recalls variable selection techniques which have been proposed in the literature. It discusses estimation techniques that can be used for implementing the selection strategies on the basis of observed data. The three sequential heuristic searches introduced, namely forward, backward, and bidirectional selection, share with the two mutual information estimators, the empirical and the Gaussian, a low computational cost coupled with a growing literature of good empirical results. Most of the selection criteria presented here use combinations of only bi- and trivariate probability distributions in order to reduce the effect of the curse of dimensionality. Finally, the chapter introduces the notions of relevance, redundancy, and synergy in order to explain and compare each method's ability to combine those bi- and trivariate distributions in an efficient setting.
    Biological Knowledge Discovery Handbook, 12/2013: pages 399-420; , ISBN: 9781118132739
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