Stressor interaction networks suggest antibiotic resistance co-opted from stress responses to temperature

Abstract

Environmental factors like temperature, pressure, and pH partly shaped the evolution of life. As life progressed, new stressors (e.g., poisons and antibiotics) arose as part of an arms race among organisms. Here we ask if cells co-opted existing mechanisms to respond to new stressors, or whether new responses evolved de novo. We use a network-clustering approach based purely on phenotypic growth measurements and interactions among the effects of stressors on population growth. We apply this method to two types of stressors-temperature and antibiotics-to discover the extent to which their cellular responses overlap in Escherichia coli. Our clustering reveals that responses to low and high temperatures are clearly separated, and each is grouped with responses to antibiotics that have similar effects to cold or heat, respectively. As further support, we use a library of transcriptional fluorescent reporters to confirm heat-shock and cold-shock genes are induced by antibiotics. We also show strains evolved at high temperatures are more sensitive to antibiotics that mimic the effects of cold. Taken together, our results strongly suggest that temperature stress responses have been co-opted to deal with antibiotic stress.

Fig. 1

Schematic illustration of the approach taken in this work. a Growth is measured in the following conditions: reference growth g_φ at the optimal temperature (T_opt = 41 °C) in the absence of drug, g_x at optimal temperature with drug, g_y at experimental temperature T_exp, but no drug, and g_xy at non-optimal temperature with drug. b The growth of each experimental condition is converted to proportions w_x, w_y, w_xy by dividing by the reference growth. The difference between w_xy (observed growth) and the product of w_x and w_y (expected growth under independence) is then used to classify the interaction between drugs and temperatures into three cases: synergistic (red line), additive (white or not shown), and antagonistic (green line), c which can be represented as an interaction network. d Drugs and temperatures can then be clustered into a functional class based on the monochromaticity of interactions with a different class. This example shows a drug-temperature interaction, but drug–drug interactions are obtained similarly, by replacing the growth with no drug at T_exp with the growth under a second drug at T_opt

Fig. 2

Monochromatic clustering of the interaction network. a Unclustered interaction network. The nodes that correspond to drugs are color-coded by their mechanism of action (Table 1), and the nodes that correspond to temperatures are colored in a gradient from blue (low) to red (high). The edges correspond to discretized interaction type, as in Fig. 3c: synergy (red), antagonism (green), additive or unknown (no edge). b Network clustered into monochromatic classes by the modified Prism2 algorithm (Supplemental Information)

Fig. 3

Interaction effects between antibiotics and temperature based on growth after 24-h. The interaction effect ($\tilde{\epsilon }$) values are color-coded in a gradient, from synergy (red) to additive (gray) and antagonism (green). a Overall distribution of the mean estimated interaction effects across all treatments. The distribution shows three clear peaks, corresponding to strong synergy, additivity, and antagonistic buffering. b Matrix heatmap of the mean interaction effects. Antibiotics with the same mechanism of action show similar interaction patterns. c Matrix heatmap of the discretized interaction types used for constructing the edges of the interaction network

Fig. 4

Gene expression of E. coli after exposure to antibiotics and high temperature. The gene expression response of E. coli was evaluated with a library of 1870 fluorescent transcriptional reporters. a Mean absolute gene expression distance between experimental conditions. Lower numbers indicate conditions with more similar gene expression profiles. b Gene expression of representative heat-shock and cold-shock genes relative to control (37 °C) in response to experimental conditions (44 °C, STR, TET). c Gene ontology terms in the biological process category over-represented in the set of over-expressed and under-expressed genes in each experimental condition relative to control. Terms that are in bold occur in more than one treatment

Fig. 5

Antibiotic sensitivity of high-temperature-adapted E. coli strains. a Absolute change in the IC50 (µg/mL) relative to the ancestral strain. Heat-adapted strains (red), rpoB mutant strains (purple). Error bars represent 95% credible intervals (CIs). Gray region represents the 95% CI of ancestral strain. Drugs are grouped according to the clusters of antibiotics and temperature in (Fig. 2). Conditions where the model fit was poor were removed from the plots (Supplemental Information). b Heatmap of log2 fold changes from the ancestral IC50. Heat-adapted strains are denoted by HA, while rpoB mutant strains are denoted by M. Positive numbers (yellow) indicate increased IC50 (more resistance), while negative numbers (blue) indicate a decreased IC50 (higher sensitivity). Drugs are grouped in the same way as in a. Missing conditions are shown in white