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A novel functional gene selection method provides a systematic view of cell migration.

ABSTRACT: Cell migration is a central process that is essential for embryonic development, wound repair, inflammatory response, homeostasis and tumor metastasis. A method of genome-wide selection based on the gain-of-function has been devised to identify novel cell migration-promoting genes in cultured cells. After the introduction of the retroviral mouse brain cDNA library into NIH3T3 mouse fibroblast cells, migration-promoted cells were selected by a three-dimensional migration assay using cell culture inserts. After five rounds of enrichment, cDNAs were retrieved from the cells that passed the selection processes. Cell migration-promoting activity was confirmed by independent migration assays for the retrieved cDNAs. Multiple cell migration-promoting genes were successfully isolated by this method. The genes identified can be used to gain a systematic view of cell migration. The gain-of-function selection method described here can be combined with RNAi-mediated loss-of-function screen or selection to be a more powerful tool for the systems biology research of cell migration.

SUBMITTER: Suk K

PROVIDER: S-EPMC2900615 | biostudies-literature | 2010 Apr-Jun

REPOSITORIES: biostudies-literature

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A novel functional gene selection method provides a systematic view of cell migration.

Suk Kyoungho K

Cell adhesion & migration 20100422 2

Cell migration is a central process that is essential for embryonic development, wound repair, inflammatory response, homeostasis and tumor metastasis. A method of genome-wide selection based on the gain-of-function has been devised to identify novel cell migration-promoting genes in cultured cells. After the introduction of the retroviral mouse brain cDNA library into NIH3T3 mouse fibroblast cells, migration-promoted cells were selected by a three-dimensional migration assay using cell culture ...[more]

PMID: 20215876

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Project description:BackgroundAmong the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest.ResultsHere, we present a method that integrates gene ontology (GO) information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied to identify the genes that play a role in the cytotoxic responses of human hepatoblastoma cell line (HepG2) to saturated fatty acid (SFA) and tumor necrosis factor (TNF)-alpha, as compared to the non-toxic response to the unsaturated FFAs (UFA) and TNF-alpha. Incorporation of prior knowledge led to a better discrimination of the toxic phenotypes from the others. The model identified roles of lysosomal ATPases and adenylate cyclase (AC9) in the toxicity of palmitate. To validate the role of AC in palmitate-treated cells, we measured the intracellular levels of cyclic AMP (cAMP). The cAMP levels were found to be significantly reduced by palmitate treatment and not by the other FFAs, in accordance with the model selection of AC9.ConclusionsA framework is presented that incorporates prior ontology information, which helped to (a) perform unsupervised clustering of the phenotypes, and (b) identify the genes relevant to each cluster of phenotypes. We demonstrate the proposed framework by applying it to identify physiologically-relevant feature genes that conferred differential toxicity to saturated vs. unsaturated FFAs. The framework can be applied to other problems to efficiently integrate ontology information and expression data in order to identify feature genes.

Project description:BackgroundIn the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different 'omics' resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency.ResultsIn this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification.ConclusionsThe experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

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A novel functional gene selection method provides a systematic view of cell migration.

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A novel functional gene selection method provides a systematic view of cell migration.

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