Abstract
Genetic interaction screens are a powerful methodology to establish novel roles for genes and elucidate functional connections between genes. Such studies have been performed to great effect in single-cell organisms such as yeast and E. coli (Schuldiner et al., 2005; Butland et al., 2008; Costanzo et al., 2010), but similar large-scale interaction studies using targeted reverse-genetic deletions in multi-cellular organisms have not been feasible. We developed a CRISPR/Cas9-based method for deleting genes in C. elegans and replacing them with a heterologous fluorescent reporter (Norris et al., 2015). Recently we took advantage of that system to perform a large-scale, reverse genetic screen using null alleles in animals for the first time, focusing on RNA binding protein genes (Norris et al., 2017). This type of approach should be similarly applicable to many other gene classes in C. elegans. Here we detail the protocols involved in generating a library of double mutants and performing medium-throughput competitive fitness assays to test for genetic interactions resulting in fitness changes.
Keywords: C. elegans, Genetics, Combinatorial genetics, RNA binding protein, Fitness
Background
Large-scale genetic interaction screens using reverse-genetic null alleles have not previously been feasible in animals. RNAi has been used to study genetic interactions in C. elegans by knocking down expression of a large number of different genes in the presence of a single mutant background (Baugh et al., 2005; Lehner et al., 2006). However, this strategy is limited by the variable efficacy of RNAi knockdown, thereby complicating the interpretation of the results. We developed a method for efficient editing of the C. elegans genome (Norris et al., 2015) and recently expanded upon that method to enable large-scale genetic interaction profiling in animals using null alleles for the first time. We focused our initial efforts on neuronally-expressed RNA binding protein genes, which have been shown in a number of cases to act combinatorially (Gracida et al., 2016; Norris et al., 2014). We found widespread genetic interactions among the set of RNA binding proteins we studied, and similar strategies should be broadly applicable to other gene classes as well.
Materials and Reagents
Equipment
Procedure
Data analysis
These data analysis steps are an expansion of those detailed in Norris et al. (2017).
Notes
Recipes
Acknowledgments
This protocol was adapted from Norris et al., 2017. Support for AN was supplied by the Floyd B. James Endowed Professorship (Southern Methodist University). Support for JC was supplied by NIH Office of the Director (NIH Early Independence Award DP5OD009153) and Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN-2017-06573). There are no conflicts of interest or competing interest.
References
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