Mapping DNA damage‐dependent genetic interactions in yeast via party mating and barcode fusion genetics

Abstract

Condition‐dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State‐of‐the‐art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double‐mutant strains, does not scale readily to multi‐condition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFG‐GI), by which double‐mutant strains generated via en masse “party” mating can also be monitored en masse for growth to detect genetic interactions. By using site‐specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG‐GI enables multiplexed quantitative tracking of double mutants via next‐generation sequencing. We applied BFG‐GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4‐nitroquinoline 1‐oxide (4NQO), bleomycin, zeocin, and three other DNA‐damaging environments. BFG‐GI recapitulated known genetic interactions and yielded new condition‐dependent genetic interactions. We validated and further explored a subnetwork of condition‐dependent genetic interactions involving MAG1,SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53.

Improved discovery of genetic interactions using CRISPRiSeq across multiple environments

“…First, for each condition we tested, fitness estimates were highly reproducible across replicate strains carrying the same guide sequence. Previously, we, and others, have reported that when generating strains for GI screening via mating and plate-based selection, suppressors or other mutations that can affect fitness commonly occur (van Leeuwen et al 2016;Jaffe et al 2017;Díaz-Mejía et al 2018). In contrast, here, for the strains generated through pooled transformation that carry inducible gRNAs, we observed that the error on fitness across replicate strains is similar to the error across replicate cultures (Fig.…”

“…SGA requires that individual strains be kept spatially segregated, and as a result, large collections are impractical to store and difficult to reassay under new conditions. Pooled assays provide a potentially powerful alternative, because they require significantly less space and resources to generate, store, and subsequently assay across conditions (Pan et al 2004;Decourty et al 2008;Jaffe et al 2017;Díaz-Mejía et al 2018). Recently, we introduced a DNA barcode-fusion-based method, iSeq, to perform GI screens in a pooled format (Jaffe et al 2017).…”

“…Although this approach made it possible to easily retest the fitness across multiple conditions, it was limited both by the two rounds of arrayed mating and selection and by the influence of segregating variation and de novo mutations that occur during strain generation, which impacted reproducibility. A related method has since been introduced, also relying on barcode fusion, that increases throughput of strain generation by performing a pooled mating to generate double mutants (Díaz-Mejía et al 2018). However, whole-genome sequencing, in these and other studies, has shown that spontaneous mutations occur often during strain generation, leading to confounding genetic variation between ostensibly replicate strains (van Leeuwen et al 2016;Jaffe et al 2017;Díaz-Mejía et al 2018).…”

“…A related method has since been introduced, also relying on barcode fusion, that increases throughput of strain generation by performing a pooled mating to generate double mutants (Díaz-Mejía et al 2018). However, whole-genome sequencing, in these and other studies, has shown that spontaneous mutations occur often during strain generation, leading to confounding genetic variation between ostensibly replicate strains (van Leeuwen et al 2016;Jaffe et al 2017;Díaz-Mejía et al 2018). These types of mutations may be partly responsible for the relatively low reproducibility previously observed across replicate SGA screens (Schuldiner et al 2005;Jasnos and Korona 2007;Dodgson et al 2016).…”

Two protein/protein interaction assays in one go

“…Protein interaction networks are highly dynamic, changing in response to cellular stimuli, chemical perturbations, splice variants, or pathogenic mutations. Much of the focus of interaction proteomics has recently shifted to characterizing these changes (Sahni et al , ; Díaz‐Mejía et al , ). Consequently, any new assay should ideally detect and measure such changes.…”