Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution.

Viruses spread between cells, tissues, and organisms by cell-free and cell-cell transmissions. Both mechanisms enhance disease development, but it is difficult to distinguish between them. Here, we analyzed the transmission mode of human adenovirus (HAdV) in monolayers of epithelial cells by wet laboratory experimentation and a computer simulation. Using live-cell fluorescence microscopy and replication-competent HAdV2 expressing green fluorescent protein, we found that the spread of infection invariably occurred after cell lysis. It was affected by convection and blocked by neutralizing antibodies but was independent of second-round infections. If cells were overlaid with agarose, convection was blocked and round plaques developed around lytic infected cells. Infected cells that did not lyse did not give rise to plaques, highlighting the importance of cell-free transmission. Key parameters for cell-free virus transmission were the time from infection to lysis, the dose of free viruses determining infection probability, and the diffusion of single HAdV particles in aqueous medium. With these parameters, we developed anin silicomodel using multiscale hybrid dynamics, cellular automata, and particle strength exchange. This so-called white box model is based on experimentally determined parameters and reproduces viral infection spreading as a function of the local concentration of free viruses. These analyses imply that the extent of lytic infections can be determined by either direct plaque assays or can be predicted by calculations of virus diffusion constants and modeling.

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As drug-resistant pathogens continue to emerge, combination therapy will increasingly be relied upon to treat infections and to help combat further development of multidrug resistance. At present a dichotomy exists between clinical practice, which favors therapeutically synergistic combinations, and the scientific model emerging from in vitro experimental work, which maintains that this interaction provides greater selective pressure toward resistance development than other interaction types. We sought to extend the current paradigm, based on work below or near minimum inhibitory concentration levels, to reflect drug concentrations more likely to be encountered during treatment. We performed a series of adaptive evolution experiments using Staphylococcus aureus. Interestingly, no relationship between drug interaction type and resistance evolution was found as resistance increased significantly beyond wild-type levels. All drug combinations, irrespective of interaction types, effectively limited resistance evolution compared with monotreatment. Cross-resistance and collateral sensitivity were found to be important factors in the extent of resistance evolution toward a combination. Comparative genomic analyses revealed that resistance to drug combinations was mediated largely by mutations in the same genes as single-drug-evolved lineages highlighting the importance of the component drugs in determining the rate of resistance evolution. Results of this work suggest that the mechanisms of resistance to constituent drugs should be the focus of future resistance evolution work.