Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs

Phenotypic convergence in bacterial adaptive evolution to ethanol stress

Stability of ethanol tolerance. Strain F at the end point (2,500 h) and at 576 h was cultivated for 200 generations absent ethanol stress. After the cultivation, ethanol tolerance was evaluated by measuring specific growth rates in 5 % ethanol stress (red bars). The growth rates under ethanol stress were similar to those before the non-stress cultivation (blue bars) and were significantly higher than that of the parent strain. (PDF 976 kb

SAGAS: Simulated annealing and greedy algorithm scheduler for laboratory automation

During laboratory automation of life science experiments, coordinating specialized instruments and human experimenters for various experimental procedures is important to minimize the execution time. In particular, the scheduling of life science experiments requires the consideration of time constraints by mutual boundaries (TCMB) and can be formulated as the “scheduling for laboratory automation in biology” (S-LAB) problem. However, existing scheduling methods for the S-LAB problems have difficulties in obtaining a feasible solution for large-size scheduling problems at a time sufficient for real-time use. In this study, we proposed a fast schedule-finding method for S-LAB problems, SAGAS (Simulated annealing and greedy algorithm scheduler). SAGAS combines simulated annealing and the greedy algorithm to find a scheduling solution with the shortest possible execution time. We have performed scheduling on real experimental protocols and shown that SAGAS can search for feasible or optimal solutions in practicable computation time for various S-LAB problems. Furthermore, the reduced computation time by SAGAS enables us to systematically search for laboratory automation with minimum execution time by simulating scheduling for various laboratory configurations. This study provides a convenient scheduling method for life science automation laboratories and presents a new possibility for designing laboratory configurations

Additional file 1: Figure S1. of Acceleration and suppression of resistance development by antibiotic combinations

MICs of evolved strains obtained under single drug application. The MICs to (a) AMK, (b) ENX, and (c) CP of the parent strain (P) and strains evolved under single drug application (AMK, ENX, and CP) are presented. Evolved strains were obtained by isolating a single clone from the end-point culture of the laboratory evolution, and used to quantify MICs. (PDF 376 kb

Additional file 2: Table S1. of Acceleration and suppression of resistance development by antibiotic combinations

Mutations fixed in the evolved strains. The list was obtained by genome resequencing analysis using Illumina Miseq. (XLSX 28 kb