Growth‐mediated negative feedback shapes quantitative antibiotic response

Abstract

Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Resource allocation in biochemically structured metabolic networks

“…), which is found by re-arranging terms in [20,21]. Hence, the expression of a given enzyme is inversely proportional to its square root specificity at low growth and to its catalytic rate  max i at high growth.…”

Diverse mutant selection windows shape spatial heterogeneity in evolving populations

“…To test this, we performed a sensitivity analysis by varying initial mutant proportion, mutation supply, and blood vessel separation and quantifying the difference between the Altieri entropy of the MSWs and the population. Mutation supply is the mutation rate times the maximum population size (10 4 ). Normalized entropy difference Δ entropy was calculated as where e MSW and e population refer to the Altieri entropy of the MSWs and the population, respectively.…”

“…Genotype-specific dose-response curves are ubiquitous across disease domains, including cancer and infectious disease. Doseresponse curves can vary between different genotypes in multiple characteristics, such as their y-intercept (drug-free growth rate), IC 50 (half-maximal inhibitory concentration), and shape [1][2][3][4][5][6][7][8][9] . Dose-response curves may also reveal fitness tradeoffs, where drug resistance imposes a fitness cost in the drug-free environment [10][11][12] .…”

How total mRNA influences cell growth

“…More in detail, the response depends on the growth condition as well as the affinity of the drug to its target (Greulich et al, 2012). The structure of this feedback has been used to predict the shape of dose-response curves for different translation-targeting antibiotics (Angermayr et al, 2022). This kind of analysis remains largely open for the case of transcription inhibitors.…”

“…Our model adds the further step of being able to compare growth-optimized with non-optimized resonse scenarios, and to make definite predictions for ribosomal and RNAP sector response to transcription inhibitors. However, additional elements such as drug affinity and feedback mechanisms (Angermayr et al, 2022; Greulich et al, 2012) may be important to fully understand the physiological response to transcription inhibitors. Future studies could extend our framework in these directions.…”