Antibiotics have been considered the single most significant medical discovery of the 20th century. Due to decades of over-prescription and misuse, however, antibiotics are losing their efficacy due to the emergence and rapid rise of antibiotic-resistant bacteria. To combat this imminent threat, in addition to developing new drugs, it is equally critical to develop strategies that enable more effective use of existing antibiotics. Doing so requires a mechanistic understanding of both short-term and long-term bacterial population dynamics in response to antibiotic treatment. An intriguing phenomenon that arises from antibiotic treatment is the post-antibiotic effect (PAE) ? after transient treatment, the growth of a bacterial population is often temporarily suppressed even after the antibiotic is removed. This phenomenon was first described in the 1940s and has since been reported in the majority of, but not all, antibiotics. Despite the prevalence of PAE, however, the underlying mechanisms are poorly understood. The objective of our proposed research is to elucidate the molecular mechanism underlying generation of PAE and, based on this understanding, to design effective antibiotic treatment protocols. In our preliminary work, we measured PAE arising from treatment of E. coli with several well-characterized antibiotics. Based on these measurements and the literature data, we hypothesized that PAE can be explained by the uptake and export kinetics of antibiotics by cells. Our proposed research will examine this hypothesis and its alternatives in depth and breadth. Moreover, we will use computation modeling to design and experimentally test antibiotic protocols to exploit PAE. In particular, we aim to design treatment protocols that achieve similar treatment efficacy while using minimal amounts of drugs. Such protocols will reduce perturbation to the native microbiota and exert less selection pressure that can drive emergence and spread of antibiotic resistance.

Public Health Relevance

The proposed research approach will provide mechanistic insights into a common phenomenon resulting from antibiotic treatment ? post antibiotic effect. While the importance of this effect is well acknowledged, its underlying mechanism is poorly understood. Furthermore, the proposed research will lead to the development and evaluation of effective treatment strategies against bacterial infections.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM098642-07
Application #
9626425
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Ainsztein, Alexandra M
Project Start
2011-09-15
Project End
2021-01-31
Budget Start
2019-02-01
Budget End
2020-01-31
Support Year
7
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Duke University
Department
Biomedical Engineering
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Tsoi, Ryan; Wu, Feilun; Zhang, Carolyn et al. (2018) Metabolic division of labor in microbial systems. Proc Natl Acad Sci U S A 115:2526-2531
Lee, Anna J; Wang, Shangying; Meredith, Hannah R et al. (2018) Robust, linear correlations between growth rates and ?-lactam-mediated lysis rates. Proc Natl Acad Sci U S A 115:4069-4074
Meredith, Hannah R; Andreani, Virgile; Ma, Helena R et al. (2018) Applying ecological resistance and resilience to dissect bacterial antibiotic responses. Sci Adv 4:eaau1873
Srimani, Jaydeep K; Huang, Shuqiang; Lopatkin, Allison J et al. (2017) Drug detoxification dynamics explain the postantibiotic effect. Mol Syst Biol 13:948
Wu, Feilun; Bethke, Jonathan H; Wang, Meidi et al. (2017) Quantitative and synthetic biology approaches to combat bacterial pathogens. Curr Opin Biomed Eng 4:116-126
Cao, Yangxiaolu; Feng, Yaying; Ryser, Marc D et al. (2017) Programmable assembly of pressure sensors using pattern-forming bacteria. Nat Biotechnol 35:1087-1093
Tanouchi, Yu; Pai, Anand; Park, Heungwon et al. (2017) Long-term growth data of Escherichia coli at a single-cell level. Sci Data 4:170036
Lopatkin, Allison J; Meredith, Hannah R; Srimani, Jaydeep K et al. (2017) Persistence and reversal of plasmid-mediated antibiotic resistance. Nat Commun 8:1689
Zhang, Carolyn; Tsoi, Ryan; You, Lingchong (2016) Addressing biological uncertainties in engineering gene circuits. Integr Biol (Camb) 8:456-64
Lopatkin, Allison J; Sysoeva, Tatyana A; You, Lingchong (2016) Dissecting the effects of antibiotics on horizontal gene transfer: Analysis suggests a critical role of selection dynamics. Bioessays 38:1283-1292

Showing the most recent 10 out of 32 publications