Fig. 4: Fitness can be accurately predicted using a single time-point based definition of entropy.

a Genome-wide differential expression (indicated as log2FoldChange Antibiotic/NDC (no drug control)) shows significantly wider distributions in antibiotic-sensitive strains (wtTIGR4 and wt19F) compared to antibiotic-adapted strains in the presence of vancomycin (a cell wall synthesis inhibitor; CWSI) and rifampicin (an RNA synthesis inhibitor; RSI), respectively in a two-sided Kolmogorov–Smirnov test. **: 0.0001 < p < 0.001; ***: p < 0.0001. b Entropy for a single time point is defined as the log-transformed variance of the distribution of differential expression across genes for a specific timepoint. c Single time point entropy is calculated from differential expression of all genes in experiments in the training (left panels) and test (right panels) datasets at each time point and plotted against time post-stress exposure (i.e., in the presence of antibiotics—AMX, CEF, CFT, CIP, COT, DAP, IMI, KAN, LIN, LVX, MOX, PEN, RIF, TET, TOB, VNC, or in the absence of nutrients—Glycine-GLY, Uracil-URA, Valine-VAL). Dashed red line indicates the entropy threshold (2.08) for the single-timepoint entropy predictions of fitness. The performance of the single time-point entropy-based fitness prediction (applied to all timepoints, ranging from 10′ to 240′) is shown as receiver-operator characteristic (ROC, d) and precision-recall (PR, e) curves. The area under the ROC curve is 0.79 and 0.88 for training and test sets, respectively. The area under the PR curve is 0.77 and 0.96 for training and test sets respectively. f Confusion matrix of single time-point entropy-based fitness prediction of the training (top panel) and test (bottom panel) datasets, highlights a good performance, but shows that there are a relatively large number of false positives. g Entropy values of individual experiments in the training (top) and test (bottom) sets, separated by time. Left and right panels show early (≤45 min) and late (>45 min) timepoints, respectively. It turns out that most false-positive predictions in panel f come from early timepoints due to a lack in transcriptional changes within the first 45′ after antibiotic exposure. In contrast, antibiotic exposure longer than 45′ (late timepoints) leads to a clear separation of high and low fitness and high accuracy in training and test data sets.