Format

Send to

Choose Destination
Nat Protoc. 2014 Aug;9(8):1867-81. doi: 10.1038/nprot.2014.127. Epub 2014 Jul 10.

Quantitative analysis of triple-mutant genetic interactions.

Author information

1
1] Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA. [2] California Institute for Quantitative Biosciences (QB3), San Francisco, California, USA.
2
Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, Massachusetts, USA.
3
1] Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA. [2] California Institute for Quantitative Biosciences (QB3), San Francisco, California, USA. [3] J. David Gladstone Institutes, San Francisco, California, USA.

Abstract

The quantitative analysis of genetic interactions between pairs of gene mutations has proven to be effective for characterizing cellular functions, but it can miss important interactions for functionally redundant genes. To address this limitation, we have developed an approach termed triple-mutant analysis (TMA). The procedure relies on a query strain that contains two deletions in a pair of redundant or otherwise related genes, which is crossed against a panel of candidate deletion strains to isolate triple mutants and measure their growth. A central feature of TMA is to interrogate mutants that are synthetically sick when two other genes are deleted but interact minimally with either single deletion. This approach has been valuable for discovering genes that restore critical functions when the principal actors are deleted. TMA has also uncovered double-mutant combinations that produce severe defects because a third protein becomes deregulated and acts in a deleterious fashion, and it has revealed functional differences between proteins presumed to act together. The protocol is optimized for Singer ROTOR pinning robots, takes 3 weeks to complete and measures interactions for up to 30 double mutants against a library of 1,536 single mutants.

PMID:
25010907
PMCID:
PMC4167031
DOI:
10.1038/nprot.2014.127
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center