TY - JOUR AB - Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. AU - Chevereau, Guillaume AU - Bollenbach, Mark Tobias ID - 1823 IS - 4 JF - Molecular Systems Biology TI - Systematic discovery of drug interaction mechanisms VL - 11 ER - TY - GEN AU - Chevereau, Guillaume AU - Lukacisinova, Marta AU - Batur, Tugce AU - Guvenek, Aysegul AU - Ayhan, Dilay Hazal AU - Toprak, Erdal AU - Bollenbach, Mark Tobias ID - 9711 TI - Excel file containing the raw data for all figures ER - TY - GEN AU - Chevereau, Guillaume AU - Lukacisinova, Marta AU - Batur, Tugce AU - Guvenek, Aysegul AU - Ayhan, Dilay Hazal AU - Toprak, Erdal AU - Bollenbach, Mark Tobias ID - 9765 TI - Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs ER - TY - JOUR AB - The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution. AU - Chevereau, Guillaume AU - Dravecka, Marta AU - Batur, Tugce AU - Guvenek, Aysegul AU - Ayhan, Dilay AU - Toprak, Erdal AU - Bollenbach, Mark Tobias ID - 1619 IS - 11 JF - PLoS Biology TI - Quantifying the determinants of evolutionary dynamics leading to drug resistance VL - 13 ER -