Gene Overexpression: Uses, Mechanisms, and Interpretation

Prelich, Gregory

doi:10.1534/genetics.111.136911

Cited by 367 publications

(335 citation statements)

References 114 publications

(128 reference statements)

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“…Doing overexpression screens in modified backgrounds will often be the reasonable strategy. Unlike loss-of-function phenotypes, interpreting overexpression phenotypes will usually be more challenging (Prelich, 2012). For example, whether an observed phenotype results from an activating or inhibiting mechanism has to be determined.…”

Section: Discussionmentioning

confidence: 99%

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A versatile platform for creating a comprehensive UAS-ORFeome library in Drosophila

et al. 2013

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SUMMARYOverexpression screens are used to explore gene functions in Drosophila, but this strategy suffers from the lack of comprehensive and systematic fly strain collections and efficient methods for generating such collections. Here, we present a strategy that could be used efficiently to generate large numbers of transgenic Drosophila strains, and a collection of 1149 UAS-ORF fly lines that were created with the site-specific ΦC31 integrase method. For this collection, we used a set of 655 genes that were cloned as two variants, either as an open reading frame (ORF) with a native stop codon or with a C-terminal 3xHA tag. To streamline the procedure for transgenic fly generation, we demonstrate the utility of injecting pools of plasmids into embryos, each plasmid containing a randomised sequence (barcode) that serves as a unique identifier for plasmids and, subsequently, fly strains. We also developed a swapping technique that facilitates the rapid exchange of promoters and epitope tags in vivo, expanding the versatility of the ORF collection. The work described here serves as the basis of a systematic library of Gal4/UAS-regulated transgenes.

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Section: Discussionmentioning

confidence: 99%

“…Wittwer et al, 2005;Prelich, 2012). So far, such screens have almost exclusively relied on random transposon insertions containing an enhancer-promoter (EP) element that can be used to drive the expression of genes flanking the insertions (Rørth, 1996;Bellen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A versatile platform for creating a comprehensive UAS-ORFeome library in Drosophila

et al. 2013

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“…Hto fusion proteins may cause dominant, GAL4-dependent phenotypes by several mechanisms (see Prelich 2012). A full-or near-full-length protein may be detrimental due to simple overexpression or when expressed in the wrong stage or cell type.…”

Section: A Vector For Simultaneous Tagging and Controlled Expressionmentioning

confidence: 99%

Inducible Protein Traps with Dominant Phenotypes for Functional Analysis of theDrosophilaGenome

et al. 2014

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The Drosophila melanogaster genome has been extensively characterized, but there remains a pressing need to associate gene products with phenotypes, subcellular localizations, and interaction partners. A multifunctional, Minos transposon-based protein trapping system called Hostile takeover (Hto) was developed to facilitate in vivo analyses of endogenous genes, including live imaging, purification of protein complexes, and mutagenesis. The Hto transposon features a UAS enhancer with a basal promoter, followed by an artificial exon 1 and a standard 59 splice site. Upon GAL4 induction, exon 1 can splice to the next exon downstream in the flanking genomic DNA, belonging to a random target gene. Exon 1 encodes a dual tag (FLAG epitope and mCherry red fluorescent protein), which becomes fused to the target protein. Hto was mobilized throughout the genome and then activated by eye-specific GAL4; an F 1 screen for abnormal eye phenotypes was used to identify inserts that express disruptive fusion proteins. Approximately 1.7% of new inserts cause eye phenotypes. Of the first 23 verified target genes, 21 can be described as regulators of cell biology and development. Most are transcription factor genes, including AP-2, CG17181, cut, klu, mamo, Sox102F, and sv. Other target genes [l(1)G0232, nuf, pum, and Syt4] make cytoplasmic proteins, and these lines produce diverse fluorescence localization patterns. Hto permits the expression of stable carboxy-terminal subfragments of proteins, which are rarely tested in conventional genetic screens. Some of these may disrupt specific cell pathways, as exemplified by truncated forms of Mastermind and Nuf.

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“…Traditionally, experimentalists delete or suppress genes to annotate them and to detect phenotypes of interest (1), and such studies are routinely conducted in model organisms. However, it has long been recognized that gene overexpression is a complementary tool for linking genotype to phenotype and usually provides phenotypes that are different from those observed in loss-of-function studies (2).…”

mentioning

confidence: 99%

“…Nonetheless, there have been only a handful of genome-wide studies addressing gene overexpression to date (2,23,24), and this stands in sharp contrast to the wealth of data available from numerous genome-wide KO or knock-down studies (1,25). For these reasons, in silico approaches to model gene overexpression and studies that apply them genome-wide are highly desirable.…”

mentioning

confidence: 99%

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Wagner¹,

Zarecki²,

Reshef

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically downregulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGEbased analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.systems metabolic engineering | metabolic modeling | constraint-based modeling | flux balance analysis | horizontal gene transfer

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Gene Overexpression: Uses, Mechanisms, and Interpretation

Cited by 367 publications

References 114 publications

A versatile platform for creating a comprehensive UAS-ORFeome library in Drosophila

A versatile platform for creating a comprehensive UAS-ORFeome library in Drosophila

Inducible Protein Traps with Dominant Phenotypes for Functional Analysis of theDrosophilaGenome

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

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