Fig. 1

Experimental strategy to characterize differential doxorubicin-gene interaction, with respect to the Warburg metabolic transition. a The phenomic model incorporates treatment of individually grown cultures of the YKO/KD collection with increasing doxorubicin (0, 2.5, 5, 7.5, and 15 ug/mL) in “fermentable/glycolytic” (HLD) or “non-fermentable/respiratory” (HLEG) media. b Representative cell array images, treated and untreated with 15 ug/mL doxorubicin. c Time series of individual culture images, exemplifying gene deletion suppression (vps54-∆0) and gene deletion enhancement (mms1-∆0), relative to parental control (“RF1”) in HLEG media with indicated concentrations (0, 5, and 15 ug/mL) of doxorubicin. d After image analysis, data time series are fit to a logistic growth function, G(t), to obtain the cell proliferation parameters (CPPs), K (carrying capacity), L (time at which K/2 is reached), and r (maximum-specific rate) for each culture. “∆L” (left panel) indicates K_i (see the “Methods” section). e Interaction is quantified by linear regression of L_i (indicated “Delta_L” and “Delta_K” in right panels; see the “Methods” section) across the entire dose range, which is converted to a z-score by dividing with the variance of the parental reference control (see the “Methods” section). f Gene interaction profiles were grouped by recursive expectation-maximization clustering (REMc) to reveal deletion-enhancing and deletion-suppressing doxorubicin-gene interaction modules and the influence of the Warburg effect. Resulting clusters were analyzed with GOTermFinder (GTF) to identify enriched biological functions. g Gene Ontology Term Averaging (GTA) was used as a complement to REMc/GTF. h The model for genetic buffering of doxorubicin cytotoxicity incorporates primary and interaction effects involving glycolysis (green), and respiration (red), to explain the influence of Warburg context (blue) on doxorubicin-gene interaction (black)