Antibiotics have been hailed as the single most significant therapeutic discovery in medicine. However, they have become increasingly ineffective due to emergence of antibiotic resistant bacteria. In addition to developing new antibiotics, there is a critical need to design better treatment protocols using existing antibiotics. Achieving this goal will require a better understanding of the myriad of ways by which bacteria can resist or tolerate antibiotic treatment at the level of individuals or populations. A common phenomenon of bacterial tolerance is the inoculum effect: for a given concentration of an antibiotic, its ability to inhibit bacterial growth decreases with the size of the bacterial inoculum. Its occurrence is often considered undesirable in the clinical setting: it can increase mortality rates of infected host due to insufficient dose of antibiotics and cause overestimation of bacterial resistance. However, the underlying mechanism by which the inoculum effect occurs remains poorly understood. For an antibiotic targeting the protein synthesis machinery (the ribosome), our preliminary analysis suggests that: (1) a critical determinant of inoculum effect is fast degradation of the ribosomal components induced by the antibiotic via the heat shock response;(2) the inoculum effect can drastically affect efficacy of antibiotic treatment. Our proposed research aims to examine these hypotheses by using a translational approach that integrates mathematical modeling, in vitro study, and in vivo study in the animal model of bacterial infections. It is our vision that the proposed work will generate mechanistic understanding of inoculum effect and lead to design of effective treatment strategies against pathogens that exhibit inoculum effect. These outcomes would represent a significant step toward more effective use of existing antibiotics to treat bacterial infections.
The proposed research approach will provide mechanistic insights into a common yet poorly understood phenomenon of non-genetic bacterial tolerance against antibiotic treatment. Furthermore, the proposed research will lead to development and evaluation effective treatment strategies against bacterial infections.
|Srimani, Jaydeep K; Huang, Shuqiang; Lopatkin, Allison J et al. (2017) Drug detoxification dynamics explain the postantibiotic effect. Mol Syst Biol 13:948|
|Lopatkin, Allison J; Meredith, Hannah R; Srimani, Jaydeep K et al. (2017) Persistence and reversal of plasmid-mediated antibiotic resistance. Nat Commun 8:1689|
|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|
|Zhang, Carolyn; Tsoi, Ryan; You, Lingchong (2016) Addressing biological uncertainties in engineering gene circuits. Integr Biol (Camb) 8:456-64|
|Huang, Shuqiang; Lee, Anna Jisu; Tsoi, Ryan et al. (2016) Coupling spatial segregation with synthetic circuits to control bacterial survival. Mol Syst Biol 12:859|
|Lopatkin, Allison J; Huang, Shuqiang; Smith, Robert P et al. (2016) Antibiotics as a selective driver for conjugation dynamics. Nat Microbiol 1:16044|
|Cao, Yangxiaolu; Ryser, Marc D; Payne, Stephen et al. (2016) Collective Space-Sensing Coordinates Pattern Scaling in Engineered Bacteria. Cell 165:620-30|
|Meredith, Hannah R; Lopatkin, Allison J; Anderson, Deverick J et al. (2015) Bacterial temporal dynamics enable optimal design of antibiotic treatment. PLoS Comput Biol 11:e1004201|
|Tanouchi, Yu; Pai, Anand; Park, Heungwon et al. (2015) A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature 523:357-60|
Showing the most recent 10 out of 27 publications