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Gene interaction

About: Gene interaction is a(n) research topic. Over the lifetime, 3697 publication(s) have been published within this topic receiving 216652 citation(s).
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Journal ArticleDOI
19 Nov 1993-Cell
TL;DR: A gene is identified, named WAF1, whose induction was associated with wild-type but not mutant p53 gene expression in a human brain tumor cell line and that could be an important mediator of p53-dependent tumor growth suppression.
Abstract: The ability of p53 to activate transcription from specific sequences suggests that genes induced by p53 may mediate its biological role as a tumor suppressor. Using a subtractive hybridization approach, we identified a gene, named WAF1, whose induction was associated with wild-type but not mutant p53 gene expression in a human brain tumor cell line. The WAF1 gene was localized to chromosome 6p21.2, and its sequence, structure, and activation by p53 was conserved in rodents. Introduction of WAF1 cDNA suppressed the growth of human brain, lung, and colon tumor cells in culture. Using a yeast enhancer trap, a p53-binding site was identified 2.4 kb upstream of WAF1 coding sequences. The WAF1 promoter, including this p53-binding site, conferred p53-dependent inducibility upon a heterologous reporter gene. These studies define a gene whose expression is directly induced by p53 and that could be an important mediator of p53-dependent tumor growth suppression.

8,173 citations


Journal ArticleDOI
22 Apr 2002-Nature Genetics
TL;DR: This work applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli, and finds that much of the network is composed of repeated appearances of three highly significant motifs.
Abstract: Little is known about the design principles1,2,3,4,5,6,7,8,9,10 of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis2,11,12, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams1,2,3,4,5,6,7,8,9,10,13, we sought to break down such networks into basic building blocks2. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli3,6. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

2,938 citations


Journal ArticleDOI
22 Jan 2010-Science
TL;DR: A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function.
Abstract: A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

2,095 citations


Journal ArticleDOI
06 Feb 2004-Science
TL;DR: Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.
Abstract: A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

1,969 citations


Journal ArticleDOI
30 Jun 1989-Cell
TL;DR: DNA clones of the wild-type p53 proto-oncogene inhibit the ability of E1Aplus ras or mutant p53 plus ras-activated oncogenes to transform primary rat embryo fibroblasts, suggesting that the p53 prototype can act negatively to block transformation.
Abstract: DNA clones of the wild-type p53 proto-oncogene inhibit the ability of E1A plus ras or mutant p53 plus ras-activated oncogenes to transform primary rat embryo fibroblasts. The rare clones of transformed foci that result from E1A plus ras plus wild-type p53 triple transfections all contain the p53 DNA in their genome, but the great majority fail to express the p53 protein. The three cell lines derived from such foci that express p53 all produce mutant p53 proteins with properties similar or identical to transformation-activated p53 proteins. The p53 mutants selected in this fashion (transformation in vitro) resemble the p53 mutants selected in tumors (in vivo). These results suggest that the p53 proto-oncogene can act negatively to block transformation.

1,922 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
2021119
2020113
2019143
2018124
2017133

Top Attributes

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Topic's top 5 most impactful authors

Jason H. Moore

8 papers, 1.1K citations

Robert C. Elston

5 papers, 194 citations

Paul W. Franks

4 papers, 113 citations

Jose M. Ordovas

4 papers, 330 citations

Yongqun He

4 papers, 125 citations