Fig. 2: Transcriptional responses separate antibiotics with different mechanisms of action.

a Principal component analysis (PCA) on differential expression datasets from sensitive S. pneumoniae strains T4 and 19F grown in the presence of 16 different antibiotics at 1× MIC depicts antibiotic responses as temporal transcriptional trajectories. Each line describes the trajectory of one of one strain in the presence of a CWSI (AMX, CEF, CFT, IMI, PEN, VNC), DSI (CIP, COT, LVX, MOX) PSI (KAN, LIN, TET, TOB), or RSI (RIF). Trajectories for each strain are largely grouped based on their MOA, and grouped-trajectories become more distinct over time. The size of each data point increases with the time of antibiotic exposure; each trajectory is split into 6 timepoints, e.g., for an experiment that spans 120′ each point indicates a 20′ increment. Abbreviations are as in Fig. 1. b In order to quantify the separation of the PCA trajectories by an antibiotic’s MOA, pairwise distances between PCA trajectories were computed (see Methods). Pairs of transcriptional trajectories obtained using drugs within the same MOA tend to have smaller distances than pairs obtained using drugs with different MOA’s. K-means clustering of the trajectory distances groups the trajectories mostly by MOA, although some PSI and CWSI trajectories are grouped with DSI ones. The top and bottom bars above the heatmap show the K-means clustering result, and the real MOA of each trajectory respectively, which have 64% agreement. c Confusion matrices indicating the performance of the gene-panel that predicts MOA. This panel was generated using a multi-class regression model (see Supplementary Table 1 for the training and test set split, and Methods for details on parameter tuning) and consists of 34 genes. The gene-panel correctly predicts the MOA on all training set data and only misclassifies a single experiment on the previously unseen test dataset, showing the different MOA’s being easily distinguishable with simple gene-based methods.