In genetics, the population is not always the sum of its components. Sometimes, a phenotype produced by the combined action of two genes will be different from the expected effect of simply adding the two genes together. Recently, the research team of Jonathan Weissman and Nevan Krogan from the University of California, San Francisco systematically explored the genetic interaction in yeast for the first time in an independent study. Relevant papers were published in the journals "Cell" and "Nature Methods". By mapping the genetic interaction of yeast, you can identify positive and negative interactions between genes and grouped genes that function in the same protein complex or signaling pathway. Weissman laboratory postdoctor Michael Bassik said: "The important information obtained from the yeast genetic interaction map makes us and others believe that they may also be useful in higher organisms. We hope to be able to observe the comprehensive lethality in disease models. Interactions are widely used to identify cancer drug targets. "

Because genome-wide knockout strains cannot be used to map genetic interactions, the researchers used two different RNAi strategies for pairwise gene knockdown. In the Cell article, Bassik and Kampmann developed a pooling method that overcomes the problems inherent in RNAi screening, such as direct off-target effects (suppression of unnecessary targets) and indirect effects (cellular RNAi machine saturation). They applied this procedure to find genes that interact to affect the response of cells to ricin.

Beginning with preliminary genome-wide RNAi screening, they targeted each protein-coding gene with approximately 25 different short hairpin RNAs (shRNAs) to infect human leukemia cells, using deep sequencing to monitor the treatment with and without ricin Enrichment of shRNAs in cell populations. This high-coverage library utilizes multiple independent shRNAs, expanding the opportunity to effectively target each gene, thereby enabling accurate assessment of the chances of shRNAs acting on each intended target. This screening generated about 200 genes that can affect the sensitivity of cellular ricin. The research team then wrote barcodes of pairs of shRNAs, bound them to these genes, and repeated the screening. Computer signal simplification allows them to identify genetic interactions that deviate from expected phenotypes. The resulting genetic interaction map summarizes well-defined complexes and also reveals unexpected results. In addition to controlling off-target effects, this hybrid strategy has several advantages. Kampmann said: "We don't need high-throughput robots. Anyone can operate our method as long as they have a cell culture chamber." It also doesn't need to consider the batch processing or positioning effects in the well analysis. The number of mixed cells can be easily changed; in addition, the use of a virus to infect cells with shRNAs is not limited to screening for easily infected cell lines. The limitation is that the phenotype needs to be evaluated in the mixed cell population, so it is impossible to observe the more subtle changes in single cells.

In order to monitor more phenotypic readings, in the Nature Methods paper, the Krogan research team collaborated with the Barbara Panning and Sourav Bandyopadhyay teams to use a different method in each individual well through high-content imaging Each gene pair was screened. Scientists have used siRNAs (esiRNAs) prepared by endonucleases. This reduces the chance of off-target effects, but requires a liquid handler to dispense two esiRNAs in each well. The researchers transfected mouse cells in trans and used imaging to monitor the effects on cell growth. They targeted 130 genes known to be involved in chromatin regulation, and rated each genetic interaction pair as positive, negative, or neutral. Krogan summarized these results. "We are very pleased to see that the genetic trends we see in simple organisms also exist in higher organisms, including positive genetic interactions related to protein interactions." Both research teams chose Investigate epistasis in disease signaling pathways first. But the Weissman team hopes to study cancer cells, and the Krogan team is interested in analyzing host-pathogen interactions. In addition, Krogan and colleagues are working to use an esiRNA library that targets non-coding RNAs to explore the interaction mechanism of these regulatory RNAs and their protein-coding acacia.

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