Episome-generated N-myc antisense RNA restricts the differentiation potential of primitive neuroectodermal cell lines.

Neuroectodermal tumors of childhood provide a unique opportunity to examine the role of genes potentially regulating neuronal growth and differentiation because many cell lines derived from these tumors are composed of at least two distinct morphologic cell types. These types display variant phenotypic characteristics and spontaneously interconvert, or transdifferentiate, in vitro. The factors that regulate transdifferentiation are unknown. Application of antisense approaches to the transdifferentiation process has allowed us to explore the precise role that N-myc may play in regulating developing systems. We now report construction of an episomally replicating expression vector designed to generate RNA antisense to part of the human N-myc gene. Such a vector is able to specifically inhibit N-myc expression in cell lines carrying both normal and amplified N-myc alleles. Inhibition of N-myc expression blocks transdifferentiation in these lines, with accumulation of cells of an intermediate phenotype. A concomitant decrease in growth rate but not loss of tumorigenicity was observed in the N-myc nonamplified cell line CHP-100. Vector-generated antisense RNA should allow identification of genes specifically regulated by the proto-oncogene N-myc.     

MiRANN: a reliable approach for improved classification of precursor microRNA using Artificial Neural Network model

MicroRNA (miRNA) is a special class of short noncoding RNA that serves pivotal function of regulating gene expression. The computational prediction of new miRNA candidates involves various methods such as learning methods and methods using expression data. This article has proposed a reliable model - miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. The number of known miRNAs is now huge and diversified that could cover almost all characteristics of unknown miRNAs which increases the quality of the result (99.9% accuracy, 99.8% sensitivity, 100% specificity) and provides a more reliable prediction. MiRANN performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. We have also tested our result using a previous negative set. MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance.     

Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning

There is a need to design computational methods to support the prediction of gene regulatory networks. Such models should offer both biologically-meaningful and computationally-accurate predictions, which in combination with other techniques may improve large-scale, integrative studies. This paper presents a new machine learning method for the prediction of putative regulatory associations from expression data, which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression dataset. The results were statistically validated and compared with the relationships inferred by two machine learning approaches to gene regulatory network prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory, large-scale studies of automated identification of potentially-relevant gene expression associations.     

Transdifferentiation--fact or artifact

Normal development appears to involve a progressive restriction in developmental potential. However, recent evidence suggests that this progressive restriction is not irreversible and can be altered to reveal novel phenotypic potentials of stem, progenitor, and even differentiated cells. While some of these results can be explained by the presence of contaminating cell populations, persistence of pluripotent stem cells, cell fusion, etc., several examples exist that are difficult to explain as anything other than "true transdifferentiation" and/or dedifferentiation. These examples of transdifferentiation are best explained by understanding how the normal process of progressive cell fate restriction occurs during development. We suggest that subversion of epigenetic controls regulating cell type specific gene expression likely underlies the process of transdifferentiation and it may be possible to identify specific factors to control the transdifferentiation process. We predict, however, that transdifferentiation will not be reliable or reproducible and will probably require complex manipulations.     

Who's your neighbor? New computational approaches for functional genomics

Several recently developed computational approaches in comparative genomics go beyond sequence comparison. By analyzing phylogenetic profiles of protein families, domain fusions, gene adjacency in genomes, and expression patterns, these methods predict many functional interactions between proteins and help deduce specific functions for numerous proteins. Although some of the resultant predictions may not be highly specific, these developments herald a new era in genomics in which the benefits of comparative analysis of the rapidly growing collection of complete genomes will become increasingly obvious.