Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples

Next-generation sequencing has proven an extremely effective technology for molecular counting applications where the number of sequence reads provides a digital readout for RNA-seq, ChIP-seq, Tn-seq and other applications. The extremely large number of sequence reads that can be obtained per run permits the analysis of increasingly complex samples. For lower complexity samples, however, a point of diminishing returns is reached when the number of counts per sequence results in oversampling with no increase in data quality. A solution to making next-generation sequencing as efficient and affordable as possible involves assaying multiple samples in a single run. Here, we report the successful 96-plexing of complex pools of DNA barcoded yeast mutants and show that such ‘Bar-seq’ assessment of these samples is comparable with data provided by barcode microarrays, the current benchmark for this application. The cost reduction and increased throughput permitted by highly multiplexed sequencing will greatly expand the scope of chemogenomics assays and, equally importantly, the approach is suitable for other sequence counting applications that could benefit from massive parallelization

Defining genetic interaction

Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction

Multiplex assay for condition-dependent changes in protein–protein interactions

Changes in protein–protein interactions that occur in response to environmental cues are difficult to uncover and have been poorly characterized to date. Here we describe a yeast-based assay that allows many binary protein interactions to be assessed in parallel and under various conditions. This method combines molecular bar-coding and tag array technology with the murine dihydrofolate reductase-based protein-fragment complementation assay. A total of 238 protein-fragment complementation assay strains, each representing a unique binary protein complex, were tagged with molecular barcodes, pooled, and then interrogated against a panel of 80 diverse small molecules. Our method successfully identified specific disruption of the Hom3:Fpr1 interaction by the immunosuppressant FK506, illustrating the assay’s capacity to identify chemical inhibitors of protein–protein interactions. Among the additional findings was specific cellular depletion of the Dst1:Rbp9 complex by the anthracycline drug doxorubicin, but not by the related drug idarubicin. The assay also revealed chemical-induced accumulation of several binary multidrug transporter complexes that largely paralleled increases in transcript levels. Further assessment of two such interactions (Tpo1:Pdr5 and Snq2:Pdr5) in the presence of 1,246 unique chemical compounds revealed a positive correlation between drug lipophilicity and the drug response in yeast