Literature DB >> 11891763

Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network.

David E Featherstone1, Kendal Broadie.   

Abstract

The vast majority (>95%) of single-gene mutations in yeast affect not only the expression of the mutant gene, but also the expression of many other genes. These data suggest the presence of a previously uncharacterized "gene expression network"--a set of interactions between genes which dictate gene expression in the native cell environment. Here, we quantitatively analyze the gene expression network revealed by microarray expression data from 273 different yeast gene deletion mutants.(1) We find that gene expression interactions form a robust, error-tolerant "scale-free" network, similar to metabolic pathways(2) and artificial networks such as power grids and the internet.(3-5) Because the connectivity between genes in the gene expression network is unevenly distributed, a scale-free organization helps make organisms resistant to the deleterious effects of mutation, and is thus highly adaptive. The existence of a gene expression network poses practical considerations for the study of gene function, since most mutant phenotypes are the result of changes in the expression of many genes. Using principles of scale-free network topology, we propose that fragmenting the gene expression network via "genome-engineering" may be a viable and practical approach to isolating gene function. Copyright 2002 Wiley Periodicals, Inc.

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Year:  2002        PMID: 11891763     DOI: 10.1002/bies.10054

Source DB:  PubMed          Journal:  Bioessays        ISSN: 0265-9247            Impact factor:   4.345


  48 in total

1.  Extensive phenotypic variation in early flowering mutants of Arabidopsis.

Authors:  Sylvie Pouteau; Valérie Ferret; Valérie Gaudin; Delphine Lefebvre; Mohammed Sabar; Gengchun Zhao; Franck Prunus
Journal:  Plant Physiol       Date:  2004-04-30       Impact factor: 8.340

2.  Coexpression analysis of human genes across many microarray data sets.

Authors:  Homin K Lee; Amy K Hsu; Jon Sajdak; Jie Qin; Paul Pavlidis
Journal:  Genome Res       Date:  2004-06       Impact factor: 9.043

3.  Genetic variation in the Yolk protein expression network of Drosophila melanogaster: sex-biased negative correlations with longevity.

Authors:  A M Tarone; L M McIntyre; L G Harshman; S V Nuzhdin
Journal:  Heredity (Edinb)       Date:  2012-07-04       Impact factor: 3.821

4.  Reverse engineering large-scale genetic networks: synthetic versus real data.

Authors:  Luwen Zhang; Mei Xiao; Yong Wang; Wu Zhang
Journal:  J Genet       Date:  2010-04       Impact factor: 1.166

Review 5.  One hundred years of pleiotropy: a retrospective.

Authors:  Frank W Stearns
Journal:  Genetics       Date:  2010-11       Impact factor: 4.562

6.  Gene network polymorphism is the raw material of natural selection: the selfish gene network hypothesis.

Authors:  Zsolt Boldogköi
Journal:  J Mol Evol       Date:  2004-09       Impact factor: 2.395

7.  Shadows of complexity: what biological networks reveal about epistasis and pleiotropy.

Authors:  Anna L Tyler; Folkert W Asselbergs; Scott M Williams; Jason H Moore
Journal:  Bioessays       Date:  2009-02       Impact factor: 4.345

8.  Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression.

Authors:  Takeshi Obayashi; Kengo Kinoshita
Journal:  DNA Res       Date:  2009-09-18       Impact factor: 4.458

Review 9.  Applications of genome-scale metabolic reconstructions.

Authors:  Matthew A Oberhardt; Bernhard Ø Palsson; Jason A Papin
Journal:  Mol Syst Biol       Date:  2009-11-03       Impact factor: 11.429

10.  Boolean implication networks derived from large scale, whole genome microarray datasets.

Authors:  Debashis Sahoo; David L Dill; Andrew J Gentles; Robert Tibshirani; Sylvia K Plevritis
Journal:  Genome Biol       Date:  2008-10-30       Impact factor: 13.583

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