Statistical analyses.

Statistical analyses were performed on MIC data derived from a 10⁻² spot dilution since this dilution achieved significant and reproducible ≤±2-fold MIC differences between pEMR-11 and pMS119EH transformants. The QCC STAC was excluded from analysis since it failed to meet a minimum ±2-fold MIC cutoff. MIC values were used in hierarchical clustering analyses and were scaled according to the equation fold change_QCC = variant_QCC/WT_QCC, where fold change_QCC is the fold change in the MIC for the QCC, which is the ratio of the mean EmrE variant MIC value (variant_QCC) divided by the mean WT EmrE MIC value (WT_QCC) for the same QCC (Table S3). Hierarchical agglomerative clustering analysis was performed on the MIC data set using R statistics software (45) and the heatmap.plus package (46). Values listed on each dendrogram show the approximately unbiased (AU) and bootstrap probability (BP) P values obtained after 100 bootstrapped replicates using the R statistics pvclust package and function (47). k-means cluster analysis was used to estimate the significance of various distance and linkage metrics, and from this analysis, a Euclidean distance method using Ward linkage resulted in the most significant number of clusters. Fourfold changes in the MIC value were determined to be the most significant (P ≤ 0.001) when those values were compared to 2-fold changes in the MIC values (P ≤ 0.05) (Fig. S2), on the basis of MIC data obtained for EmrE E14D/E14A (Table S3); hence, the results for 11 QCCs were selected for final assessment (Fig. 1). Venn diagrams of correlated QCC chemicals or properties and EmrE variants were generated using the R statistics VennDiagram package and function (48), using the selection criteria described in the Fig. 3 legend.