Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Abstract

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

“… ( A ) Growth landscape (per capita growth rate relative to untreated cells) as a function of drug concentration for two drugs (drug 1, tigecycline; drug 2, ciprofloxacin; concentrations measured in μg/mL) based on measurements in Dean et al, 2020 . We consider evolution of resistance in a population exposed to a fixed external drug concentration ( (white asterisk).…”

“…These interactions may be leveraged to improve treatments – for example, by offering enhanced antimicrobial effects at reduced concentrations. But these interactions can also accelerate, reduce, or even reverse the evolution of resistance ( Chait et al, 2007 ; Michel et al, 2008 ; Hegreness et al, 2008 ; Pena-Miller et al, 2013 ; Dean et al, 2020 ), leading to tradeoffs between short-term inhibitory effects and long-term evolutionary potential ( Torella et al, 2010 ). In addition, resistance to one drug may be associated with modulated resistance to other drugs.…”

Hidden suppressive interactions are common in higher-order drug combinations

“…However, for over a century, the idea of one drug being an antidote for another was mostly ignored. More recently, in the last decade and a half, there has been a renewed interest in this phenomenon of suppression ( Yeh et al., 2006 ; Chait et al., 2007 ; Cokol et al., 2014 ; de Vos and Bollenbach, 2014 ; Bollenbach, 2015 ; Singh and Yeh, 2017 ; Lukačišin and Bollenbach, 2019 ; Tyers and Wright, 2019 ; Dean et al., 2020 ). Suppression was first defined in terms of antibiotic interactions when a systematic study of 2-drug interactions in 21 antibiotics was conducted, and approximately 10% of all interactions fell into the category of suppression ( Yeh et al., 2006 ).…”

Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

“…To do so, standard practice calls for aggressive drug treatment to rapidly remove the drug-sensitive pathogen population and prevent resistance-conferring mutations [7][8][9][10][11][12][13][14][15][16][17]. Aggressive treatment can involve either single-drug or combination therapies, which have been shown to modulate the emergence of resistance [18][19][20][21][22][23][24][25]. Here, we are interested in situations in which such aggressive regimens do not completely prevent the emergence of resistance-for example, scenarios in which resistance is already present at the onset of treatment.…”