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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    • "A “correlation network” [38,39] approach has recently been developed whereby the nodes represent constituent elements such as metabolites, neurons, or genes, and the links represent the correlation of a characteristic of the elements such as metabolic flux, neuronal activity, or gene expression above a threshold level. This method has been explicitly, or at least implicitly, employed in various studies on metabolomics [38-40], neurodynamics [41], and transcriptomes based on gene co-expression [42,43]. Likewise, this method can be applied to explore the modules of morphological structures (termed morphological correlation network), whereby the nodes represent the constituent parts and the links represent the correlation of the spatial positions among them. "
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    ABSTRACT: One of the most intriguing questions in evolutionary developmental biology is how an insect acquires a mimicry pattern within its body parts. A striking example of pattern mimicry is found in the pattern diversity of moth and butterfly wings, which is thought to evolve from preexisting elements illustrated by the nymphalid ground plan (NGP). Previous studies demonstrated that individuality of the NGP facilitates the decoupling of associated common elements, leading to divergence. In contrast, recent studies on the concept of modularity have argued the importance of a combination of coupling and decoupling of the constituent elements. Here, we examine the modularity of a mimicry wing pattern in a moth and explore an evolvable characteristic of the NGP. This study examined the wings of the noctuid moth Oraesia excavata, which closely resemble leaves with a leaf venation pattern. Based on a comparative morphological procedure, we found that this leaf pattern was formed by the NGP common elements. Using geometric morphometrics combined with network analysis, we found that each of the modules in the leaf pattern integrates the constituent components of the leaf venation pattern (i.e., the main and lateral veins). Moreover, the detected modules were established by coupling different common elements and decoupling even a single element into different modules. The modules of the O. excavata wing pattern were associated with leaf mimicry, not with the individuality of the NGP common elements. For comparison, we also investigated the modularity of a nonmimetic pattern in the noctuid moth Thyas juno. Quantitative analysis demonstrated that the modules of the T. juno wing pattern regularly corresponded to the individuality of the NGP common elements, unlike those in the O. excavata wing pattern. This study provides the first evidence for modularity in a leaf mimicry pattern. The results suggest that the evolution of this pattern involves coupling and decoupling processes to originate these modules, free from the individuality of the NGP system. We propose that this evolution has been facilitated by a versatile characteristic of the NGP, allowing the association of freely modifiable subordinate common elements to make modules.
    BMC Evolutionary Biology 07/2013; 13(1):158. DOI:10.1186/1471-2148-13-158 · 3.37 Impact Factor
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