Quantifying the determinants of evolutionary dynamics leading to drug resistance

The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution

Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution

Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations' resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics

Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance

<div><p>The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide <i>Escherichia coli</i> gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.</p></div

Resistance variability is similar for diverse antibiotics but extremely low for nitrofurantoin.

<p>(A) Schematic: the effective drug concentration <i>c</i>_eff experienced by each mutant is inferred from its response <i>R</i> via the WT dose-response curve (arrows). This transforms the DFE (<i>y</i>-axis) into the DEC of the drug (<i>x</i>-axis); the dose-sensitivity <i>n</i> determines the change in distribution width as shown. (B) DEC for different antibiotics; arrows show IQR; effective drug concentrations are normalized to the actual concentration. (C) DEC width (IQR) for different antibiotics. (D) Width of the distribution of relative IC₅₀ changes determined directly from dose-response curves of 78 deletion mutants (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s002" target="_blank">S1 Fig</a>). Error bars show bootstrap standard error (C) and 95% confidence interval from bootstrap (D), respectively; lighter bars show distribution width resulting from measurement noise alone (Materials and Methods). Note that the difference for chloramphenicol between panels C and D is not significant. Numerical data is in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s001" target="_blank">S1 Data</a>.</p

Fitness variability changes drastically in the presence of different antibiotics.

<p>(A) Sample growth curves (optical density over time) of wild type (WT; black) and gene deletion mutants <i>pdxJ</i> (green) and <i>iscS</i> (red); yellow lines are exponential fits (Materials and Methods). (B) Histogram of growth rates (i.e., approximated DFE) of ~4,000 gene deletion strains in the absence of drug; histogram of 476 WT replicates is outlined in black. (C) DFE in the presence of the antibiotics trimethoprim, nitrofurantoin, tetracycline, chloramphenicol, ciprofloxacin, mecillinam, cefoxitin, and ampicillin (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.t001" target="_blank">Table 1</a>); vertical black lines show median of WT replicates; drugs were used at concentrations inhibiting WT growth by one-third. Growth rates are normalized to median of WT in the absence of a drug. The interquartile ranges (IQRs) of the DFEs are shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.g002" target="_blank">Fig 2B</a>. Numerical data is in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s001" target="_blank">S1 Data</a>.</p

The drug-specific dose-sensitivity is robust to genetic perturbations and correlates with fitness variability.

<p>(A) Dose-response curves for eight antibiotics; circles (●) show trimethoprim; pluses (<b>+</b>), tetracycline; downward triangles (▼), chloramphenicol; stars (★), nitrofurantoin; squares (■), ciprofloxacin; leftward triangles (◄), cefoxitin; triangles (▲), mecillinam; rightward triangles (►), ampicillin. Dose-sensitivity <i>n</i> is shown (Materials and Methods). (B) Scatterplot of dose-sensitivity <i>n</i> and DFE width (IQR); Pearson’s <i>ρ</i> = 0.96, <i>p</i> = 1.3 × 10⁻⁴; <i>n</i> error bars show standard deviation of replicates; DFE width error bars show bootstrap 95% confidence interval (Materials and Methods). Horizontal dashed line shows DFE width in the absence of drug (<i>cf</i>. <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.g001" target="_blank">Fig 1B</a>). Gray line shows a linear relation as a guide to the eye. (C) Mecillinam dose-response curves for 78 arbitrary deletion mutants (purple; see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s001" target="_blank">S1 Data</a>) and 17 WT replicates (black). (D) Same data as in C with concentration rescaled to IC₅₀ and growth rate response rescaled to <i>g</i>₀ (Materials and Methods). See also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s002" target="_blank">S1 Fig</a> Numerical data is in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s001" target="_blank">S1 Data</a>.</p

Resistance variability affects the dynamics of evolutionary adaptation to drugs.

<p>(A) Simulation results from a theoretical model of resistance evolution in a morbidostat [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.ref011" target="_blank">11</a>]: IC₅₀ increase over time for a drug with narrow DEC and two available large-effect mutations (magenta) or none (gray); light lines are sample runs; dark lines are mean of 200 runs; inset: distribution of relative IC₅₀ changes used in simulations (Materials and Methods). (B) Same as in panel A for wider DEC (Materials and Methods). (C, D) Results from morbidostat laboratory evolution experiments: IC₅₀ increase over time for nitrofurantoin (C) and chloramphenicol (D); light lines are individual runs; dark lines are mean, error bars standard deviation; shaded region in C indicates early phase during which large-effect mutations fix (Materials and Methods). (E) Mutated loci in nitrofurantoin (left) and chloramphenicol (right)-resistant clones after 10 and 21 d, respectively. Filled pie segments show evolution replicates in which genes were mutated; (P) indicates promoter mutations. Bar chart shows diversity (entropy) of mutations under nitrofurantoin (magenta) and chloramphenicol (gray); <i>p</i> < 0.002 (**) and <i>p</i> < 0.0003 (***) from two-sample <i>t</i> test; error bars show jackknife standard error (Materials and Methods). Numerical data is in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s001" target="_blank">S1 Data</a>. Whole genome sequencing results are in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s009" target="_blank">S2 Table</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002299#pbio.1002299.s010" target="_blank">S3 Table</a>.</p